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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| likes
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| library_name
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nakcnx/OTG-Math-680 | nakcnx | 2023-03-25T21:34:48Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"openthaigpt",
"th",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-03-25T11:43:19Z | ---
license: apache-2.0
language:
- th
pipeline_tag: text-generation
library_name: transformers
tags:
- openthaigpt
widget:
- text: "คำถาม: ซื้อมะม่วงมา 30ลูก ระหว่างกลับบ้านหล่นไป 15ลูก เลยจอดรถเก็บมาได้ 5ลูก ให้เพื่อนไป 7ผล ปลอกกินไป 2ลูก เน่าไปอีก 3ลูก ของเก่าอยู่ในตู้เย็นอีก 3ลูก จะมีมะม่วงเท่าไร"
- text: "คำถาม: จงหาเลขคู่ที่มากกว่าหรือเท่ากับ 10 แต่น้อยกว่าหรือเท่ากับ 20"
- text: "คำถาม: จงหาค่า x ในสมการ (x/2) + 7 = 10"
- text: "คำถาม: ถ้ามีเพื่อนซื้อเนื้อวัวไป 5 กิโลกรัม และจ่ายราคา 300 บาทต่อกิโลกรัม จะต้องจ่ายเงินรวมทั้งสิ้นเท่าไร"
- text: "คำถาม: ถ้ามีสามเหลี่ยมที่มีด้านเท่ากันทั้งหมด ความยาวด้านของแต่ละด้านคือ 4 เซนติเมตร จงหาพื้นที่ของสามเหลี่ยมนั้น"
---
# OTG-Math-680
This model is fine-tuned version of [Open Thai GPT](https://huggingface.co/kobkrit/openthaigpt-gpt2-pantipwiki-poc-0.0.1) with Thai Math QA 680 pairs dataset (GSM8K, GPT-3.5 Generated, Chain of Thought). |
Absie/a2c-AntBulletEnv-v0 | Absie | 2023-03-25T21:15:05Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T21:13:50Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1760.70 +/- 86.57
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
cleanrl/MontezumaRevenge-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T21:10:05Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"MontezumaRevenge-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T21:10:03Z | ---
tags:
- MontezumaRevenge-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MontezumaRevenge-v5
type: MontezumaRevenge-v5
metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **MontezumaRevenge-v5**
This is a trained model of a PPO agent playing MontezumaRevenge-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id MontezumaRevenge-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/MontezumaRevenge-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/MontezumaRevenge-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/MontezumaRevenge-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id MontezumaRevenge-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'MontezumaRevenge-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
aimarsg/prueba3 | aimarsg | 2023-03-25T20:29:47Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-03-25T19:54:16Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: prueba3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# prueba3
This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2158
- Precision: 0.7162
- Recall: 0.6335
- F1: 0.6723
- Accuracy: 0.9737
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.75e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 29 | 0.2562 | 0.7732 | 0.5976 | 0.6742 | 0.9719 |
| No log | 2.0 | 58 | 0.2526 | 0.705 | 0.5618 | 0.6253 | 0.9704 |
| No log | 3.0 | 87 | 0.2187 | 0.6833 | 0.6534 | 0.6680 | 0.9705 |
| No log | 4.0 | 116 | 0.2205 | 0.6583 | 0.6295 | 0.6436 | 0.9715 |
| No log | 5.0 | 145 | 0.2161 | 0.7162 | 0.6534 | 0.6833 | 0.9712 |
| No log | 6.0 | 174 | 0.2293 | 0.6977 | 0.5976 | 0.6438 | 0.9722 |
| No log | 7.0 | 203 | 0.2207 | 0.6972 | 0.6056 | 0.6482 | 0.9724 |
| No log | 8.0 | 232 | 0.2343 | 0.6781 | 0.6295 | 0.6529 | 0.9707 |
| No log | 9.0 | 261 | 0.2212 | 0.7115 | 0.5896 | 0.6449 | 0.9730 |
| No log | 10.0 | 290 | 0.2171 | 0.7260 | 0.6016 | 0.6580 | 0.9734 |
| No log | 11.0 | 319 | 0.2191 | 0.6851 | 0.6414 | 0.6626 | 0.9725 |
| No log | 12.0 | 348 | 0.2101 | 0.7056 | 0.6494 | 0.6763 | 0.9740 |
| No log | 13.0 | 377 | 0.2227 | 0.7240 | 0.6375 | 0.6780 | 0.9732 |
| No log | 14.0 | 406 | 0.2226 | 0.7442 | 0.6375 | 0.6867 | 0.9739 |
| No log | 15.0 | 435 | 0.2247 | 0.7339 | 0.6375 | 0.6823 | 0.9739 |
| No log | 16.0 | 464 | 0.2167 | 0.6983 | 0.6454 | 0.6708 | 0.9729 |
| No log | 17.0 | 493 | 0.2220 | 0.7281 | 0.6295 | 0.6752 | 0.9732 |
| 0.0005 | 18.0 | 522 | 0.2294 | 0.7299 | 0.6135 | 0.6667 | 0.9725 |
| 0.0005 | 19.0 | 551 | 0.2104 | 0.6949 | 0.6534 | 0.6735 | 0.9722 |
| 0.0005 | 20.0 | 580 | 0.2103 | 0.7240 | 0.6375 | 0.6780 | 0.9730 |
| 0.0005 | 21.0 | 609 | 0.2092 | 0.7137 | 0.6454 | 0.6778 | 0.9735 |
| 0.0005 | 22.0 | 638 | 0.2091 | 0.7181 | 0.6494 | 0.6820 | 0.9737 |
| 0.0005 | 23.0 | 667 | 0.2081 | 0.7162 | 0.6534 | 0.6833 | 0.9735 |
| 0.0005 | 24.0 | 696 | 0.2198 | 0.7264 | 0.6135 | 0.6652 | 0.9722 |
| 0.0005 | 25.0 | 725 | 0.2206 | 0.7290 | 0.6215 | 0.6710 | 0.9725 |
| 0.0005 | 26.0 | 754 | 0.2194 | 0.7256 | 0.6215 | 0.6695 | 0.9735 |
| 0.0005 | 27.0 | 783 | 0.2220 | 0.7290 | 0.6215 | 0.6710 | 0.9739 |
| 0.0005 | 28.0 | 812 | 0.2230 | 0.7290 | 0.6215 | 0.6710 | 0.9735 |
| 0.0005 | 29.0 | 841 | 0.2163 | 0.7182 | 0.6295 | 0.6709 | 0.9737 |
| 0.0005 | 30.0 | 870 | 0.2158 | 0.7162 | 0.6335 | 0.6723 | 0.9737 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
impira/layoutlm-invoices | impira | 2023-03-25T20:21:25Z | 7,780 | 182 | transformers | [
"transformers",
"pytorch",
"safetensors",
"layoutlm",
"document-question-answering",
"pdf",
"invoices",
"en",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
]
| document-question-answering | 2022-09-06T17:49:13Z | ---
language: en
license: cc-by-nc-sa-4.0
pipeline_tag: document-question-answering
tags:
- layoutlm
- document-question-answering
- pdf
- invoices
widget:
- text: "What is the invoice number?"
src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
- text: "What is the purchase amount?"
src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg"
---
# LayoutLM for Invoices
This is a fine-tuned version of the multi-modal [LayoutLM](https://aka.ms/layoutlm) model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of
invoices as well as both [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [DocVQA](https://www.docvqa.org/) for general comprehension.
## Non-consecutive tokens
Unlike other QA models, which can only extract consecutive tokens (because they predict the start and end of a sequence), this model can predict longer-range, non-consecutive sequences with an additional
classifier head. For example, QA models often encounter this failure mode:
### Before
### After
However this model is able to predict non-consecutive tokens and therefore the address correctly:
## Getting started with the model
The best way to use this model is via [DocQuery](https://github.com/impira/docquery).
## About us
This model was created by the team at [Impira](https://www.impira.com/).
|
emmuzoo/ppo-SnowballTarget | emmuzoo | 2023-03-25T20:20:22Z | 20 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-03-25T20:20:17Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: emmuzoo/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
naeisher/a2c-AntBulletEnv-v0 | naeisher | 2023-03-25T19:59:31Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T19:58:21Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 851.30 +/- 36.85
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
aimarsg/prueba2 | aimarsg | 2023-03-25T19:45:42Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-03-25T18:29:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: prueba2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# prueba2
This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1829
- Precision: 0.7232
- Recall: 0.6454
- F1: 0.6821
- Accuracy: 0.9744
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 32
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 29 | 0.1726 | 0.7014 | 0.5896 | 0.6407 | 0.9720 |
| No log | 2.0 | 58 | 0.1712 | 0.6090 | 0.6454 | 0.6267 | 0.9679 |
| No log | 3.0 | 87 | 0.1665 | 0.6746 | 0.6773 | 0.6759 | 0.9720 |
| No log | 4.0 | 116 | 0.1945 | 0.7042 | 0.5976 | 0.6466 | 0.9719 |
| No log | 5.0 | 145 | 0.1850 | 0.6927 | 0.6016 | 0.6439 | 0.9724 |
| No log | 6.0 | 174 | 0.1872 | 0.6570 | 0.6335 | 0.6450 | 0.9697 |
| No log | 7.0 | 203 | 0.2014 | 0.7527 | 0.5578 | 0.6407 | 0.9730 |
| No log | 8.0 | 232 | 0.1696 | 0.6706 | 0.6733 | 0.6720 | 0.9727 |
| No log | 9.0 | 261 | 0.1743 | 0.6820 | 0.6494 | 0.6653 | 0.9730 |
| No log | 10.0 | 290 | 0.1686 | 0.6735 | 0.6574 | 0.6653 | 0.9730 |
| No log | 11.0 | 319 | 0.1868 | 0.6934 | 0.5857 | 0.6350 | 0.9712 |
| No log | 12.0 | 348 | 0.1930 | 0.7089 | 0.6016 | 0.6509 | 0.9727 |
| No log | 13.0 | 377 | 0.1826 | 0.7087 | 0.6494 | 0.6778 | 0.9730 |
| No log | 14.0 | 406 | 0.1920 | 0.7103 | 0.6056 | 0.6538 | 0.9722 |
| No log | 15.0 | 435 | 0.1848 | 0.6402 | 0.6733 | 0.6563 | 0.9712 |
| No log | 16.0 | 464 | 0.1843 | 0.6822 | 0.6414 | 0.6612 | 0.9734 |
| No log | 17.0 | 493 | 0.1874 | 0.7009 | 0.6255 | 0.6611 | 0.9730 |
| 0.0016 | 18.0 | 522 | 0.1844 | 0.6736 | 0.6494 | 0.6613 | 0.9730 |
| 0.0016 | 19.0 | 551 | 0.1850 | 0.7273 | 0.6375 | 0.6794 | 0.9744 |
| 0.0016 | 20.0 | 580 | 0.1737 | 0.7179 | 0.6693 | 0.6928 | 0.9749 |
| 0.0016 | 21.0 | 609 | 0.1798 | 0.7376 | 0.6494 | 0.6907 | 0.9747 |
| 0.0016 | 22.0 | 638 | 0.1797 | 0.7174 | 0.6574 | 0.6861 | 0.9739 |
| 0.0016 | 23.0 | 667 | 0.1783 | 0.7046 | 0.6653 | 0.6844 | 0.9742 |
| 0.0016 | 24.0 | 696 | 0.1784 | 0.7301 | 0.6574 | 0.6918 | 0.9745 |
| 0.0016 | 25.0 | 725 | 0.1818 | 0.7352 | 0.6414 | 0.6851 | 0.9745 |
| 0.0016 | 26.0 | 754 | 0.1823 | 0.7419 | 0.6414 | 0.6880 | 0.9745 |
| 0.0016 | 27.0 | 783 | 0.1786 | 0.7205 | 0.6574 | 0.6875 | 0.9749 |
| 0.0016 | 28.0 | 812 | 0.1781 | 0.7051 | 0.6574 | 0.6804 | 0.9734 |
| 0.0016 | 29.0 | 841 | 0.1802 | 0.7181 | 0.6494 | 0.6820 | 0.9744 |
| 0.0016 | 30.0 | 870 | 0.1801 | 0.7174 | 0.6574 | 0.6861 | 0.9749 |
| 0.0016 | 31.0 | 899 | 0.1824 | 0.7232 | 0.6454 | 0.6821 | 0.9745 |
| 0.0016 | 32.0 | 928 | 0.1829 | 0.7232 | 0.6454 | 0.6821 | 0.9744 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Nazzyk/Reinforce-Pixelcopter-PLE-v0 | Nazzyk | 2023-03-25T19:38:46Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T13:09:00Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 33.50 +/- 26.03
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
mrm8488/mobilebert-uncased-finetuned-squadv1 | mrm8488 | 2023-03-25T19:26:44Z | 32 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"mobilebert",
"question-answering",
"en",
"dataset:squad",
"arxiv:2004.02984",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- squad
---
# MobileBERT + SQuAD (v1.1) 📱❓
[mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) fine-tuned on [SQUAD v2.0 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
**MobileBERT** is a thin version of *BERT_LARGE*, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.
The checkpoint used here is the original MobileBert Optimized Uncased English: (uncased_L-24_H-128_B-512_A-4_F-4_OPT) checkpoint.
More about the model [here](https://arxiv.org/abs/2004.02984)
## Details of the downstream task (Q&A) - Dataset 📚
**S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD v1.1 contains **100,000+** question-answer pairs on **500+** articles.
## Model training 🏋️
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
```bash
python transformers/examples/question-answering/run_squad.py \
--model_type bert \
--model_name_or_path 'google/mobilebert-uncased' \
--do_eval \
--do_train \
--do_lower_case \
--train_file '/content/dataset/train-v1.1.json' \
--predict_file '/content/dataset/dev-v1.1.json' \
--per_gpu_train_batch_size 16 \
--learning_rate 3e-5 \
--num_train_epochs 5 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir '/content/output' \
--overwrite_output_dir \
--save_steps 1000
```
It is important to say that this models converges much faster than other ones. So, it is also cheap to fine-tune.
## Test set Results 🧾
| Metric | # Value |
| ------ | --------- |
| **EM** | **82.33** |
| **F1** | **89.64** |
| **Size**| **94 MB** |
### Model in action 🚀
Fast usage with **pipelines**:
```python
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/mobilebert-uncased-finetuned-squadv1')
QnA_pipeline({
'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
'question': 'Who did identified it ?'
})
# Output: {'answer': 'scientists.', 'end': 106, 'score': 0.7885545492172241, 'start': 96}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
Jieming/al_rm_checkpoint | Jieming | 2023-03-25T19:22:59Z | 0 | 0 | null | [
"pytorch",
"generated_from_trainer",
"license:mit",
"region:us"
]
| null | 2023-03-25T17:56:39Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: al_rm_checkpoint
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# al_rm_checkpoint
This model is a fine-tuned version of [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
golightly/rl_course_vizdoom_health_gathering_supreme | golightly | 2023-03-25T19:11:57Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T19:11:50Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.26 +/- 5.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r golightly/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
ROGRANMAR/my_awesome_asr_mind_model | ROGRANMAR | 2023-03-25T19:09:55Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-03-25T16:27:38Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_asr_mind_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_asr_mind_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
HiTZ/alpaca-lora-30b-en-pt-es-ca-eu-gl-at | HiTZ | 2023-03-25T18:55:53Z | 0 | 0 | null | [
"generated_from_trainer",
"dataset:HiTZ/alpaca_mt",
"license:other",
"region:us"
]
| null | 2023-03-23T19:51:21Z | ---
license: other
tags:
- generated_from_trainer
datasets:
- HiTZ/alpaca_mt
model-index:
- name: alpaca-lora-30b-en-pt-es-ca-eu-gl-at
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# alpaca-lora-30b-en-pt-es-ca-eu-gl-at
This model is a fine-tuned version of [decapoda-research/llama-30b-hf](https://huggingface.co/decapoda-research/llama-30b-hf) on the HiTZ/alpaca_mt ['en', 'pt', 'es', 'ca', 'eu', 'gl', 'at'] dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9088
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 21
- total_train_batch_size: 126
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1695 | 0.04 | 100 | 1.1716 |
| 1.1211 | 0.07 | 200 | 1.0964 |
| 1.0591 | 0.11 | 300 | 1.0590 |
| 1.0234 | 0.14 | 400 | 1.0341 |
| 1.0345 | 0.18 | 500 | 1.0165 |
| 0.9932 | 0.22 | 600 | 1.0024 |
| 0.9948 | 0.25 | 700 | 0.9895 |
| 1.01 | 0.29 | 800 | 0.9794 |
| 0.9488 | 0.32 | 900 | 0.9708 |
| 0.9518 | 0.36 | 1000 | 0.9627 |
| 0.9463 | 0.4 | 1100 | 0.9557 |
| 0.956 | 0.43 | 1200 | 0.9498 |
| 0.9521 | 0.47 | 1300 | 0.9437 |
| 0.9345 | 0.51 | 1400 | 0.9385 |
| 0.9469 | 0.54 | 1500 | 0.9337 |
| 0.9466 | 0.58 | 1600 | 0.9297 |
| 0.9403 | 0.61 | 1700 | 0.9257 |
| 0.9179 | 0.65 | 1800 | 0.9219 |
| 0.9468 | 0.69 | 1900 | 0.9190 |
| 0.9173 | 0.72 | 2000 | 0.9163 |
| 0.9172 | 0.76 | 2100 | 0.9142 |
| 0.9351 | 0.79 | 2200 | 0.9124 |
| 0.9238 | 0.83 | 2300 | 0.9110 |
| 0.9057 | 0.87 | 2400 | 0.9099 |
| 0.9309 | 0.9 | 2500 | 0.9093 |
| 0.8893 | 0.94 | 2600 | 0.9090 |
| 0.9095 | 0.97 | 2700 | 0.9088 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
harshil128/Reinforce-cartpole-V2 | harshil128 | 2023-03-25T18:46:41Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T18:46:36Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole-V2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 451.60 +/- 145.20
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Jieming/rm_checkpoint | Jieming | 2023-03-25T18:39:45Z | 0 | 0 | null | [
"pytorch",
"generated_from_trainer",
"license:mit",
"region:us"
]
| null | 2023-03-25T17:56:23Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: rm_checkpoint
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rm_checkpoint
This model is a fine-tuned version of [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
harshil128/ppo-LunarLander-v2 | harshil128 | 2023-03-25T18:28:38Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-01-21T21:02:19Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 227.30 +/- 81.70
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
samzoozi/ppo-LunarLander-v2 | samzoozi | 2023-03-25T18:26:35Z | 6 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T18:26:10Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.35 +/- 20.74
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Liborn/Libor | Liborn | 2023-03-25T18:26:28Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"climate",
"aa",
"dataset:fka/awesome-chatgpt-prompts",
"arxiv:1910.09700",
"license:openrail",
"region:us"
]
| null | 2023-03-25T18:17:47Z | ---
license: openrail
datasets:
- fka/awesome-chatgpt-prompts
language:
- aa
metrics:
- accuracy
library_name: adapter-transformers
tags:
- climate
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
aimarsg/prueba1 | aimarsg | 2023-03-25T18:25:04Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-03-25T17:47:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: prueba1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# prueba1
This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1842
- Precision: 0.7072
- Recall: 0.6255
- F1: 0.6638
- Accuracy: 0.9724
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3.5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 32
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 29 | 0.1520 | 0.5625 | 0.6813 | 0.6162 | 0.9659 |
| No log | 2.0 | 58 | 0.1552 | 0.6293 | 0.5817 | 0.6046 | 0.9686 |
| No log | 3.0 | 87 | 0.1586 | 0.6667 | 0.5737 | 0.6167 | 0.9709 |
| No log | 4.0 | 116 | 0.1595 | 0.6981 | 0.5896 | 0.6393 | 0.9722 |
| No log | 5.0 | 145 | 0.1699 | 0.6729 | 0.5737 | 0.6194 | 0.9676 |
| No log | 6.0 | 174 | 0.1753 | 0.6577 | 0.5817 | 0.6173 | 0.9689 |
| No log | 7.0 | 203 | 0.1665 | 0.6540 | 0.6175 | 0.6352 | 0.9681 |
| No log | 8.0 | 232 | 0.1792 | 0.7157 | 0.5618 | 0.6295 | 0.9712 |
| No log | 9.0 | 261 | 0.1682 | 0.7048 | 0.5896 | 0.6421 | 0.9714 |
| No log | 10.0 | 290 | 0.1732 | 0.7366 | 0.6016 | 0.6623 | 0.9724 |
| No log | 11.0 | 319 | 0.1663 | 0.672 | 0.6693 | 0.6707 | 0.9725 |
| No log | 12.0 | 348 | 0.1882 | 0.7071 | 0.5578 | 0.6236 | 0.9692 |
| No log | 13.0 | 377 | 0.1825 | 0.7103 | 0.6056 | 0.6538 | 0.9710 |
| No log | 14.0 | 406 | 0.1755 | 0.7164 | 0.5737 | 0.6372 | 0.9709 |
| No log | 15.0 | 435 | 0.1950 | 0.6842 | 0.5697 | 0.6217 | 0.9689 |
| No log | 16.0 | 464 | 0.1660 | 0.7240 | 0.6375 | 0.6780 | 0.9727 |
| No log | 17.0 | 493 | 0.1833 | 0.7255 | 0.5896 | 0.6505 | 0.9724 |
| 0.0061 | 18.0 | 522 | 0.1832 | 0.7190 | 0.6016 | 0.6551 | 0.9702 |
| 0.0061 | 19.0 | 551 | 0.1762 | 0.6828 | 0.6175 | 0.6485 | 0.9707 |
| 0.0061 | 20.0 | 580 | 0.1785 | 0.7346 | 0.6175 | 0.6710 | 0.9734 |
| 0.0061 | 21.0 | 609 | 0.1791 | 0.7093 | 0.6414 | 0.6736 | 0.9739 |
| 0.0061 | 22.0 | 638 | 0.1843 | 0.7476 | 0.6255 | 0.6811 | 0.9737 |
| 0.0061 | 23.0 | 667 | 0.1837 | 0.7371 | 0.6255 | 0.6767 | 0.9734 |
| 0.0061 | 24.0 | 696 | 0.1867 | 0.7176 | 0.6175 | 0.6638 | 0.9715 |
| 0.0061 | 25.0 | 725 | 0.1844 | 0.7089 | 0.6016 | 0.6509 | 0.9710 |
| 0.0061 | 26.0 | 754 | 0.1815 | 0.7072 | 0.6255 | 0.6638 | 0.9725 |
| 0.0061 | 27.0 | 783 | 0.1822 | 0.7021 | 0.6574 | 0.6790 | 0.9737 |
| 0.0061 | 28.0 | 812 | 0.1853 | 0.7048 | 0.6375 | 0.6695 | 0.9732 |
| 0.0061 | 29.0 | 841 | 0.1845 | 0.7069 | 0.6534 | 0.6791 | 0.9735 |
| 0.0061 | 30.0 | 870 | 0.1827 | 0.7004 | 0.6614 | 0.6803 | 0.9735 |
| 0.0061 | 31.0 | 899 | 0.1850 | 0.7014 | 0.6175 | 0.6568 | 0.9719 |
| 0.0061 | 32.0 | 928 | 0.1842 | 0.7072 | 0.6255 | 0.6638 | 0.9724 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
MarcusAGray/ppo-PyramidsTraining | MarcusAGray | 2023-03-25T17:58:57Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:58:52Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: MarcusAGray/ppo-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
stoked/pulsar | stoked | 2023-03-25T17:58:49Z | 0 | 0 | asteroid | [
"asteroid",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"license:afl-3.0",
"region:us"
]
| null | 2023-03-25T17:57:53Z | ---
license: afl-3.0
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- code_eval
library_name: asteroid
--- |
cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T17:58:42Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Enduro-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:58:40Z | ---
tags:
- Enduro-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Enduro-v5
type: Enduro-v5
metrics:
- type: mean_reward
value: 2317.90 +/- 109.39
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Enduro-v5**
This is a trained model of a PPO agent playing Enduro-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Enduro-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Enduro-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Enduro-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Frostbite-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T17:55:17Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Frostbite-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:55:15Z | ---
tags:
- Frostbite-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Frostbite-v5
type: Frostbite-v5
metrics:
- type: mean_reward
value: 314.00 +/- 18.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Frostbite-v5**
This is a trained model of a PPO agent playing Frostbite-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Frostbite-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Frostbite-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Frostbite-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Frostbite-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Frostbite-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Frostbite-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T17:52:56Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Enduro-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:52:54Z | ---
tags:
- Enduro-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Enduro-v5
type: Enduro-v5
metrics:
- type: mean_reward
value: 2344.70 +/- 18.42
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Enduro-v5**
This is a trained model of a PPO agent playing Enduro-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Enduro-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Enduro-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Enduro-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/FishingDerby-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T17:52:35Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"FishingDerby-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:52:34Z | ---
tags:
- FishingDerby-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FishingDerby-v5
type: FishingDerby-v5
metrics:
- type: mean_reward
value: 27.80 +/- 10.89
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **FishingDerby-v5**
This is a trained model of a PPO agent playing FishingDerby-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id FishingDerby-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id FishingDerby-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'FishingDerby-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T17:52:09Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Enduro-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:52:08Z | ---
tags:
- Enduro-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Enduro-v5
type: Enduro-v5
metrics:
- type: mean_reward
value: 2241.30 +/- 284.69
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Enduro-v5**
This is a trained model of a PPO agent playing Enduro-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Enduro-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Enduro-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Enduro-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Enduro-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/FishingDerby-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T17:51:28Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"FishingDerby-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:51:27Z | ---
tags:
- FishingDerby-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FishingDerby-v5
type: FishingDerby-v5
metrics:
- type: mean_reward
value: 27.00 +/- 11.59
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **FishingDerby-v5**
This is a trained model of a PPO agent playing FishingDerby-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id FishingDerby-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/FishingDerby-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id FishingDerby-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'FishingDerby-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T17:49:05Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"UpNDown-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:49:04Z | ---
tags:
- UpNDown-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: UpNDown-v5
type: UpNDown-v5
metrics:
- type: mean_reward
value: 200052.00 +/- 60214.62
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **UpNDown-v5**
This is a trained model of a PPO agent playing UpNDown-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id UpNDown-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id UpNDown-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'UpNDown-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
sagorsarker/emailgenerator | sagorsarker | 2023-03-25T17:48:24Z | 32 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"email-generation",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-03-29T13:14:08Z | ---
language: en
tags:
- email-generation
license: mit
---
EmailGenerator is a gpt-2 fine-tuned text-generation pre-trained model trained on [emailblog](https://www.kaggle.com/datasets/mikeschmidtavemac/emailblog) datasets for [EmailWriter](https://github.com/sagorbrur/EmailWriter) repositories.
For details about this model check [EmailWriter](https://github.com/sagorbrur/EmailWriter) repository.
|
cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T17:47:43Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"UpNDown-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:47:41Z | ---
tags:
- UpNDown-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: UpNDown-v5
type: UpNDown-v5
metrics:
- type: mean_reward
value: 189488.00 +/- 65579.51
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **UpNDown-v5**
This is a trained model of a PPO agent playing UpNDown-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id UpNDown-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id UpNDown-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'UpNDown-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T17:47:40Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"UpNDown-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:47:38Z | ---
tags:
- UpNDown-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: UpNDown-v5
type: UpNDown-v5
metrics:
- type: mean_reward
value: 191595.00 +/- 74974.86
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **UpNDown-v5**
This is a trained model of a PPO agent playing UpNDown-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id UpNDown-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/UpNDown-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id UpNDown-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'UpNDown-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
526christian/526Mix-less-crunch-test | 526christian | 2023-03-25T17:47:18Z | 6 | 0 | diffusers | [
"diffusers",
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-03-25T01:53:37Z | ---
license: creativeml-openrail-m
---
|
butchland/unit8-LunarLander-v2 | butchland | 2023-03-25T17:46:20Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:46:14Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -138.81 +/- 79.09
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'butchland/unit8-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
emilianJR/haruna_lora | emilianJR | 2023-03-25T17:44:29Z | 5 | 0 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-03-25T08:14:39Z |
---
license: creativeml-openrail-m
base_model: andite/anything-v4.0
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/kubanemil/haruna_lora
These are LoRA adaption weights for https://huggingface.co/kubanemil/haruna_lora. The weights were fine-tuned on the Haruna Sakura's images dataset. You can find some example images in the following.
|
cleanrl/WizardOfWor-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T17:32:01Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"WizardOfWor-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:31:59Z | ---
tags:
- WizardOfWor-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: WizardOfWor-v5
type: WizardOfWor-v5
metrics:
- type: mean_reward
value: 5430.00 +/- 3764.85
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **WizardOfWor-v5**
This is a trained model of a PPO agent playing WizardOfWor-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id WizardOfWor-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/WizardOfWor-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id WizardOfWor-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'WizardOfWor-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Tennis-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T17:30:41Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Tennis-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:30:40Z | ---
tags:
- Tennis-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Tennis-v5
type: Tennis-v5
metrics:
- type: mean_reward
value: 22.00 +/- 1.41
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Tennis-v5**
This is a trained model of a PPO agent playing Tennis-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Tennis-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Tennis-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Tennis-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Tennis-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Tennis-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Tennis-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Tennis-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T17:30:23Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Tennis-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:30:21Z | ---
tags:
- Tennis-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Tennis-v5
type: Tennis-v5
metrics:
- type: mean_reward
value: 22.60 +/- 1.11
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Tennis-v5**
This is a trained model of a PPO agent playing Tennis-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Tennis-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Tennis-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Tennis-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Tennis-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Tennis-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Tennis-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Venture-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T17:29:55Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Venture-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:29:54Z | ---
tags:
- Venture-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Venture-v5
type: Venture-v5
metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Venture-v5**
This is a trained model of a PPO agent playing Venture-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Venture-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Venture-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Venture-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Venture-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T17:28:24Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Venture-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:28:22Z | ---
tags:
- Venture-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Venture-v5
type: Venture-v5
metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Venture-v5**
This is a trained model of a PPO agent playing Venture-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Venture-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Venture-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Venture-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Venture-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
dmenini/ppo-Pyramids | dmenini | 2023-03-25T17:27:17Z | 10 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:34:40Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: dmenini/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
artbreguez/q-CartPole-v1 | artbreguez | 2023-03-25T17:26:28Z | 0 | 0 | null | [
"CartPole-v1",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:08:10Z | ---
tags:
- CartPole-v1
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Q-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 94.79 +/- 12.53
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **CartPole-v1**
This is a trained model of a **Q-Learning** agent playing **CartPole-v1** .
## Usage
```python
model = load_from_hub(repo_id="artbreguez/Q-Cartpole-v1", filename="q-learning.pkl")
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T17:23:29Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Surround-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:23:27Z | ---
tags:
- Surround-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Surround-v5
type: Surround-v5
metrics:
- type: mean_reward
value: 6.30 +/- 2.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Surround-v5**
This is a trained model of a PPO agent playing Surround-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Surround-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Surround-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Surround-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T17:23:03Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Surround-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:23:02Z | ---
tags:
- Surround-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Surround-v5
type: Surround-v5
metrics:
- type: mean_reward
value: 5.30 +/- 1.35
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Surround-v5**
This is a trained model of a PPO agent playing Surround-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Surround-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Surround-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Surround-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
LarryAIDraw/tentenCharacterLohaFullckpt_loha | LarryAIDraw | 2023-03-25T17:22:23Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-03-25T13:05:25Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/21305/tenten-character-lohafullckpt |
cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T17:22:19Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Surround-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:22:17Z | ---
tags:
- Surround-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Surround-v5
type: Surround-v5
metrics:
- type: mean_reward
value: 5.30 +/- 2.69
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Surround-v5**
This is a trained model of a PPO agent playing Surround-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Surround-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Surround-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Surround-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Surround-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
LarryAIDraw/teaAnzuDsodVerYuGiOh_anzudsodv1 | LarryAIDraw | 2023-03-25T17:22:11Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-03-25T13:07:16Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/18612/teaanzu-dsod-ver-or-yu-gi-oh |
mlewand/rl_course_vizdoom_health_gathering_supreme | mlewand | 2023-03-25T17:17:19Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-02-26T16:20:32Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.99 +/- 4.96
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r mlewand/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
cleanrl/SpaceInvaders-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T17:16:50Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"SpaceInvaders-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:16:49Z | ---
tags:
- SpaceInvaders-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvaders-v5
type: SpaceInvaders-v5
metrics:
- type: mean_reward
value: 7318.50 +/- 6248.69
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **SpaceInvaders-v5**
This is a trained model of a PPO agent playing SpaceInvaders-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id SpaceInvaders-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/SpaceInvaders-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/SpaceInvaders-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/SpaceInvaders-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id SpaceInvaders-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'SpaceInvaders-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/StarGunner-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T17:16:30Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"StarGunner-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:16:28Z | ---
tags:
- StarGunner-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: StarGunner-v5
type: StarGunner-v5
metrics:
- type: mean_reward
value: 66420.00 +/- 7673.43
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **StarGunner-v5**
This is a trained model of a PPO agent playing StarGunner-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id StarGunner-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id StarGunner-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'StarGunner-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
droid22/poca-SoccerTwos | droid22 | 2023-03-25T17:15:41Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:15:35Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: droid22/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
cleanrl/StarGunner-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T17:14:09Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"StarGunner-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:14:07Z | ---
tags:
- StarGunner-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: StarGunner-v5
type: StarGunner-v5
metrics:
- type: mean_reward
value: 69590.00 +/- 6098.60
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **StarGunner-v5**
This is a trained model of a PPO agent playing StarGunner-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id StarGunner-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/StarGunner-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id StarGunner-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'StarGunner-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Solaris-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T17:08:03Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Solaris-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:08:01Z | ---
tags:
- Solaris-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Solaris-v5
type: Solaris-v5
metrics:
- type: mean_reward
value: 2348.00 +/- 645.24
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Solaris-v5**
This is a trained model of a PPO agent playing Solaris-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Solaris-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Solaris-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Solaris-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Solaris-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T17:07:54Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Solaris-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:07:53Z | ---
tags:
- Solaris-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Solaris-v5
type: Solaris-v5
metrics:
- type: mean_reward
value: 2068.00 +/- 1014.94
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Solaris-v5**
This is a trained model of a PPO agent playing Solaris-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Solaris-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Solaris-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Solaris-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Solaris-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/NameThisGame-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T17:05:08Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"NameThisGame-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:05:06Z | ---
tags:
- NameThisGame-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: NameThisGame-v5
type: NameThisGame-v5
metrics:
- type: mean_reward
value: 11098.00 +/- 1705.77
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **NameThisGame-v5**
This is a trained model of a PPO agent playing NameThisGame-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id NameThisGame-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/NameThisGame-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/NameThisGame-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/NameThisGame-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id NameThisGame-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'NameThisGame-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T17:04:27Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"KungFuMaster-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:04:25Z | ---
tags:
- KungFuMaster-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: KungFuMaster-v5
type: KungFuMaster-v5
metrics:
- type: mean_reward
value: 29710.00 +/- 7182.96
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **KungFuMaster-v5**
This is a trained model of a PPO agent playing KungFuMaster-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id KungFuMaster-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id KungFuMaster-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'KungFuMaster-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T17:04:05Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Krull-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:04:04Z | ---
tags:
- Krull-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Krull-v5
type: Krull-v5
metrics:
- type: mean_reward
value: 7596.00 +/- 1556.09
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Krull-v5**
This is a trained model of a PPO agent playing Krull-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Krull-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Krull-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Krull-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T17:04:05Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"KungFuMaster-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:04:03Z | ---
tags:
- KungFuMaster-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: KungFuMaster-v5
type: KungFuMaster-v5
metrics:
- type: mean_reward
value: 25720.00 +/- 5122.27
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **KungFuMaster-v5**
This is a trained model of a PPO agent playing KungFuMaster-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id KungFuMaster-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id KungFuMaster-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'KungFuMaster-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T17:04:04Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Krull-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:04:03Z | ---
tags:
- Krull-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Krull-v5
type: Krull-v5
metrics:
- type: mean_reward
value: 7096.00 +/- 1767.62
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Krull-v5**
This is a trained model of a PPO agent playing Krull-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Krull-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Krull-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Krull-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T17:04:04Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"KungFuMaster-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:04:02Z | ---
tags:
- KungFuMaster-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: KungFuMaster-v5
type: KungFuMaster-v5
metrics:
- type: mean_reward
value: 19080.00 +/- 6065.28
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **KungFuMaster-v5**
This is a trained model of a PPO agent playing KungFuMaster-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id KungFuMaster-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id KungFuMaster-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'KungFuMaster-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T17:02:53Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Krull-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:02:52Z | ---
tags:
- Krull-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Krull-v5
type: Krull-v5
metrics:
- type: mean_reward
value: 7739.00 +/- 993.06
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Krull-v5**
This is a trained model of a PPO agent playing Krull-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Krull-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Krull-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Krull-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Krull-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
MarcusAGray/ppo-SnowballTarget | MarcusAGray | 2023-03-25T17:02:18Z | 12 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:02:13Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: MarcusAGray/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
cleanrl/MsPacman-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T17:01:09Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"MsPacman-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T17:01:08Z | ---
tags:
- MsPacman-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MsPacman-v5
type: MsPacman-v5
metrics:
- type: mean_reward
value: 2760.00 +/- 950.80
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **MsPacman-v5**
This is a trained model of a PPO agent playing MsPacman-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id MsPacman-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/MsPacman-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/MsPacman-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/MsPacman-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id MsPacman-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'MsPacman-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Kangaroo-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T16:59:27Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Kangaroo-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:59:26Z | ---
tags:
- Kangaroo-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Kangaroo-v5
type: Kangaroo-v5
metrics:
- type: mean_reward
value: 20.00 +/- 60.00
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Kangaroo-v5**
This is a trained model of a PPO agent playing Kangaroo-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Kangaroo-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Kangaroo-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Kangaroo-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Kangaroo-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T16:59:15Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Kangaroo-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:59:13Z | ---
tags:
- Kangaroo-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Kangaroo-v5
type: Kangaroo-v5
metrics:
- type: mean_reward
value: 1600.00 +/- 282.84
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Kangaroo-v5**
This is a trained model of a PPO agent playing Kangaroo-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Kangaroo-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Kangaroo-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Kangaroo-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Kangaroo-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Jamesbond-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T16:54:41Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Jamesbond-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:54:40Z | ---
tags:
- Jamesbond-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Jamesbond-v5
type: Jamesbond-v5
metrics:
- type: mean_reward
value: 465.00 +/- 128.55
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Jamesbond-v5**
This is a trained model of a PPO agent playing Jamesbond-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Jamesbond-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Jamesbond-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Jamesbond-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Jamesbond-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Jamesbond-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Jamesbond-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Jamesbond-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T16:50:35Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Jamesbond-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:50:33Z | ---
tags:
- Jamesbond-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Jamesbond-v5
type: Jamesbond-v5
metrics:
- type: mean_reward
value: 490.00 +/- 124.10
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Jamesbond-v5**
This is a trained model of a PPO agent playing Jamesbond-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Jamesbond-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Jamesbond-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Jamesbond-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Jamesbond-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Jamesbond-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Jamesbond-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
vocabtrimmer/mt5-small-trimmed-fr-60000-frquad-qa | vocabtrimmer | 2023-03-25T16:44:14Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question answering",
"fr",
"dataset:lmqg/qg_frquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-03-19T16:55:12Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: fr
datasets:
- lmqg/qg_frquad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: En quelle année a-t-on trouvé trace d'un haut fourneau similaire?, context: Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu."
example_title: "Question Answering Example 1"
- text: "question: Comment appelle-t-on la Guerre de 14-18 ?, context: Ce black dog peut être lié à des évènements traumatisants issus du monde extérieur, tels que son renvoi de l'Amirauté après la catastrophe des Dardanelles, lors de la Grande Guerre de 14-18, ou son rejet par l'électorat en juillet 1945. On sait également que dans ces deux cas, la guérison, certes lente et douloureuse et jamais complète ni définitive, se fera grâce à la peinture. D'un autre côté, étant donnés les symptômes de ce mal que Churchill éprouvait de plus en plus, il ne pouvait rien moins qu'être purement associé à de telles causes extrinsèques, ce qui correspond au profil classique de la dépression majeure unipolaire ou bipolaire."
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-fr-60000-frquad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_frquad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 10.43
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 22.59
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 17.44
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 86.74
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 66.71
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 34.34
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 20.01
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-fr-60000-frquad-qa`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-fr-60000](https://huggingface.co/ckpts/mt5-small-trimmed-fr-60000) for question answering task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [ckpts/mt5-small-trimmed-fr-60000](https://huggingface.co/ckpts/mt5-small-trimmed-fr-60000)
- **Language:** fr
- **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="fr", model="vocabtrimmer/mt5-small-trimmed-fr-60000-frquad-qa")
# model prediction
answers = model.answer_q(list_question="En quelle année a-t-on trouvé trace d'un haut fourneau similaire?", list_context=" Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-fr-60000-frquad-qa")
output = pipe("question: En quelle année a-t-on trouvé trace d'un haut fourneau similaire?, context: Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-60000-frquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_frquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 20.01 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| AnswerF1Score | 34.34 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| BERTScore | 86.74 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_1 | 17.96 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_2 | 14.51 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_3 | 12.22 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_4 | 10.43 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| METEOR | 17.44 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| MoverScore | 66.71 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| ROUGE_L | 22.59 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_frquad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: ckpts/mt5-small-trimmed-fr-60000
- max_length: 512
- max_length_output: 32
- epoch: 24
- batch: 32
- lr: 0.0005
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-60000-frquad-qa/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
cleanrl/DemonAttack-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T16:27:22Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"DemonAttack-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:27:20Z | ---
tags:
- DemonAttack-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: DemonAttack-v5
type: DemonAttack-v5
metrics:
- type: mean_reward
value: 105099.50 +/- 31017.43
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **DemonAttack-v5**
This is a trained model of a PPO agent playing DemonAttack-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id DemonAttack-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id DemonAttack-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'DemonAttack-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/DemonAttack-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T16:26:41Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"DemonAttack-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:26:39Z | ---
tags:
- DemonAttack-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: DemonAttack-v5
type: DemonAttack-v5
metrics:
- type: mean_reward
value: 88149.00 +/- 42555.30
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **DemonAttack-v5**
This is a trained model of a PPO agent playing DemonAttack-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id DemonAttack-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id DemonAttack-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'DemonAttack-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/BeamRider-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T16:22:51Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"BeamRider-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:22:49Z | ---
tags:
- BeamRider-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BeamRider-v5
type: BeamRider-v5
metrics:
- type: mean_reward
value: 5486.40 +/- 3101.98
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **BeamRider-v5**
This is a trained model of a PPO agent playing BeamRider-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id BeamRider-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/BeamRider-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/BeamRider-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/BeamRider-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id BeamRider-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'BeamRider-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/BeamRider-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T16:22:31Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"BeamRider-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:22:29Z | ---
tags:
- BeamRider-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BeamRider-v5
type: BeamRider-v5
metrics:
- type: mean_reward
value: 4463.00 +/- 1967.26
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **BeamRider-v5**
This is a trained model of a PPO agent playing BeamRider-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id BeamRider-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/BeamRider-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/BeamRider-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/BeamRider-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id BeamRider-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'BeamRider-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Centipede-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T16:21:13Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Centipede-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:21:12Z | ---
tags:
- Centipede-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Centipede-v5
type: Centipede-v5
metrics:
- type: mean_reward
value: 2054.30 +/- 809.44
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Centipede-v5**
This is a trained model of a PPO agent playing Centipede-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Centipede-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Centipede-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Centipede-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Centipede-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Centipede-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Centipede-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T16:19:47Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Berzerk-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:19:46Z | ---
tags:
- Berzerk-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Berzerk-v5
type: Berzerk-v5
metrics:
- type: mean_reward
value: 503.00 +/- 76.16
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Berzerk-v5**
This is a trained model of a PPO agent playing Berzerk-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Berzerk-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Berzerk-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Berzerk-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T16:19:41Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Berzerk-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:19:39Z | ---
tags:
- Berzerk-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Berzerk-v5
type: Berzerk-v5
metrics:
- type: mean_reward
value: 518.00 +/- 109.34
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Berzerk-v5**
This is a trained model of a PPO agent playing Berzerk-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Berzerk-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Berzerk-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Berzerk-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T16:19:30Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Berzerk-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:19:29Z | ---
tags:
- Berzerk-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Berzerk-v5
type: Berzerk-v5
metrics:
- type: mean_reward
value: 541.00 +/- 126.37
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Berzerk-v5**
This is a trained model of a PPO agent playing Berzerk-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Berzerk-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Berzerk-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Berzerk-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Berzerk-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/CrazyClimber-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T16:19:22Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"CrazyClimber-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:19:21Z | ---
tags:
- CrazyClimber-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CrazyClimber-v5
type: CrazyClimber-v5
metrics:
- type: mean_reward
value: 110540.00 +/- 10599.08
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **CrazyClimber-v5**
This is a trained model of a PPO agent playing CrazyClimber-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id CrazyClimber-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/CrazyClimber-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/CrazyClimber-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/CrazyClimber-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id CrazyClimber-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'CrazyClimber-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Defender-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T16:18:57Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Defender-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:18:52Z | ---
tags:
- Defender-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Defender-v5
type: Defender-v5
metrics:
- type: mean_reward
value: 55745.00 +/- 14538.10
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Defender-v5**
This is a trained model of a PPO agent playing Defender-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Defender-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Defender-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Defender-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Defender-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Defender-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Defender-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/CrazyClimber-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T16:18:47Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"CrazyClimber-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:18:45Z | ---
tags:
- CrazyClimber-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CrazyClimber-v5
type: CrazyClimber-v5
metrics:
- type: mean_reward
value: 90690.00 +/- 18685.85
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **CrazyClimber-v5**
This is a trained model of a PPO agent playing CrazyClimber-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id CrazyClimber-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/CrazyClimber-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/CrazyClimber-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/CrazyClimber-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id CrazyClimber-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'CrazyClimber-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Defender-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T16:18:33Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Defender-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:18:32Z | ---
tags:
- Defender-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Defender-v5
type: Defender-v5
metrics:
- type: mean_reward
value: 46740.00 +/- 14657.79
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Defender-v5**
This is a trained model of a PPO agent playing Defender-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Defender-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Defender-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Defender-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Defender-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Defender-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Defender-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
Shivraj8615/Reinforce-Pixelcopter | Shivraj8615 | 2023-03-25T16:08:11Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:08:05Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 59.10 +/- 36.98
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
202k/10-5-5 | 202k | 2023-03-25T16:04:35Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-03-25T15:23:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 10-5-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 10-5-5
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5508
- Accuracy: 0.7273
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.5508 | 0.7273 |
| No log | 2.0 | 2 | 0.5481 | 0.7273 |
| No log | 3.0 | 3 | 0.5427 | 0.7273 |
| No log | 4.0 | 4 | 0.5439 | 0.7273 |
| No log | 5.0 | 5 | 0.5448 | 0.7273 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
droid22/ppo-Pyramids | droid22 | 2023-03-25T16:01:06Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-03-25T16:01:01Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: droid22/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
202k/10-1-9 | 202k | 2023-03-25T15:45:56Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-03-25T15:13:43Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 10-1-9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 10-1-9
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6288
- Accuracy: 0.6111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.6288 | 0.6111 |
| No log | 2.0 | 2 | 0.6255 | 0.5556 |
| No log | 3.0 | 3 | 0.6228 | 0.6111 |
| No log | 4.0 | 4 | 0.6212 | 0.6111 |
| No log | 5.0 | 5 | 0.6207 | 0.6111 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
cleanrl/Seaquest-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T15:44:41Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Seaquest-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T15:44:39Z | ---
tags:
- Seaquest-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Seaquest-v5
type: Seaquest-v5
metrics:
- type: mean_reward
value: 1760.00 +/- 15.49
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Seaquest-v5**
This is a trained model of a PPO agent playing Seaquest-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --env-id Seaquest-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Seaquest-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Seaquest-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Seaquest-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_impala_atari_wrapper.py --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Seaquest-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Seaquest-v5',
'exp_name': 'cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Seaquest-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T15:44:32Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Seaquest-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T15:44:31Z | ---
tags:
- Seaquest-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Seaquest-v5
type: Seaquest-v5
metrics:
- type: mean_reward
value: 1770.00 +/- 16.12
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Seaquest-v5**
This is a trained model of a PPO agent playing Seaquest-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --env-id Seaquest-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Seaquest-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Seaquest-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Seaquest-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_impala_atari_wrapper.py --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Seaquest-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Seaquest-v5',
'exp_name': 'cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
pszemraj/flan-t5-small-instructiongen | pszemraj | 2023-03-25T15:44:03Z | 22 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"instructiongen",
"self-instruct",
"instruction generation",
"dataset:pszemraj/fleece2instructions",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-03-20T02:25:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
- instructiongen
- self-instruct
- instruction generation
datasets:
- pszemraj/fleece2instructions
metrics:
- rouge
model-index:
- name: flan-t5-small-instructiongen
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pszemraj/fleece2instructions
type: pszemraj/fleece2instructions
split: validation
metrics:
- name: Rouge1
type: rouge
value: 52.201
widget:
- text: >-
You'll need to start by choosing the right venue. Consider the type of
atmosphere and the size of the area that will be suitable for the number of
guests you plan to invite. Choose the right decorations based on your
brother's interests, such as balloons in his favorite colors, banners, and
streamers. Next, decide on the food and drinks, making sure they are tasty
and appropriate for the occasion. Then decide on the other games, music, and
entertainment that will make the party memorable. Finally, involve your
brother's friends and family to help create the perfect surprise.
example_title: birthday party
- text: 1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo
example_title: ice cream
- text: >-
Start by selecting a scale model of a building that fits the theme. Use a
hobby knife and glue to cut and assemble the model into a ruined or
abandoned version of itself, adding details like broken windows and
graffiti. Create a base for the diorama using foam, plaster, or other
materials, and paint it to resemble a ruined street or sidewalk. Add
miniature vehicles, debris, and figures to complete the scene, and use
weathering techniques like dry brushing and rust washes to add realism.
Display the diorama in a shadow box or other protective case to showcase
your work.
example_title: Miniature diorama creation
- text: >-
Start by selecting clothing that is futuristic and edgy, such as leather
jackets, neon-colored accessories, and tech-inspired patterns. Add
accessories like goggles, cybernetic implants, and LED lights to enhance the
cyberpunk vibe. Use makeup and body paint to create a futuristic look, such
as metallic skin or neon makeup. Consider adding functional elements to your
costume, such as a built-in backpack or hidden pockets for your tech
gadgets. Finally, practice your confident walk and embrace your inner
cyberpunk for a memorable and immersive costume experience.
example_title: Cyberpunk costume design
- text: >-
Start by creating a base terrain with mountains, valleys, and other natural
features. Use fractal noise and displacement mapping to add texture and
detail to the terrain, and experiment with different materials like rock,
grass, and water. Add surreal elements like floating islands, giant
mushrooms, or impossible geometry to create a dreamlike atmosphere. Use
lighting and color grading to enhance the mood and tone of the scene, and
render the final image at a high resolution for maximum impact. Share your
surreal landscape with the world and inspire others to explore the
possibilities of 3D art.
example_title: Surreal 3D landscape creation
- text: >-
Start by setting a realistic goal and creating a training plan. Build up
your mileage gradually over time, and incorporate cross-training and
strength exercises to prevent injury and improve endurance. Be sure to stay
hydrated and properly fuel your body with nutritious foods. Listen to your
body and adjust your training as needed to avoid overexertion or burnout.
Finally, taper your training in the weeks leading up to the race to give
your body time to rest and recover before the big day.
example_title: Marathon training
pipeline_tag: text2text-generation
---
# flan-t5-small-instructiongen
Instead of generating questions from text, generate instructions for LLMs!
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3401
- Rouge1: 52.201
- Rouge2: 35.6154
- Rougel: 50.2334
- Rougelsum: 50.338
- Gen Len: 14.0450
## Intended uses & limitations
This is just a **small** model/example. There is likely to be even better performance with larger models (ex [pszemraj/bart-base-instructiongen)](https://huggingface.co/pszemraj/bart-base-instructiongen) generalizes better)
Additionally, this was trained on a dataset of **only** instructions+outputs, with the `inputs` filtered out. This means that text of *1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo* will **not** get you *"Rank the following ice cream flavors: oreo, mint chip, chocolate chip, cookies and cream"*.
## Training and evaluation data
See the linked dataset `pszemraj/fleece2instructions` - it is a filtered/formatted version of `tatsu-lab/alpaca` to generate instructions for arbitrary text.
- Some of the API examples are intentionally weird to demonstrate the generalizability of the model.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.6161 | 1.0 | 181 | 1.3714 | 51.1003 | 34.5701 | 49.1277 | 49.2466 | 13.8357 |
| 1.539 | 2.0 | 362 | 1.3401 | 52.201 | 35.6154 | 50.2334 | 50.338 | 14.0450 | |
israel-avihail/ppo-SnowballTarget | israel-avihail | 2023-03-25T15:43:26Z | 12 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-03-25T15:43:21Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: israel-avihail/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Akashpb13/Kabyle_xlsr | Akashpb13 | 2023-03-25T15:40:54Z | 26 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"sw",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"kab",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-03-02T23:29:04Z | ---
language:
- kab
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- sw
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/Kabyle_xlsr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: kab
metrics:
- name: Test WER
type: wer
value: 0.3188425282720088
- name: Test CER
type: cer
value: 0.09443079928558358
---
# Akashpb13/Kabyle_xlsr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets):
- Loss: 0.159032
- Wer: 0.187934
## Model description
"facebook/wav2vec2-xls-r-300m" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Kabyle train.tsv. Only 50,000 records were sampled randomly and trained due to huge size of dataset.
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0
## Training procedure
For creating the training dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000096
- train_batch_size: 8
- seed: 13
- gradient_accumulation_steps: 4
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|-------|---------------|-----------------|----------|
| 500 | 7.199800 | 3.130564 | 1.000000 |
| 1000 | 1.570200 | 0.718097 | 0.734682 |
| 1500 | 0.850800 | 0.524227 | 0.640532 |
| 2000 | 0.712200 | 0.468694 | 0.603454 |
| 2500 | 0.651200 | 0.413833 | 0.573025 |
| 3000 | 0.603100 | 0.403680 | 0.552847 |
| 3500 | 0.553300 | 0.372638 | 0.541719 |
| 4000 | 0.537200 | 0.353759 | 0.531191 |
| 4500 | 0.506300 | 0.359109 | 0.519601 |
| 5000 | 0.479600 | 0.343937 | 0.511336 |
| 5500 | 0.479800 | 0.338214 | 0.503948 |
| 6000 | 0.449500 | 0.332600 | 0.495221 |
| 6500 | 0.439200 | 0.323905 | 0.492635 |
| 7000 | 0.434900 | 0.310417 | 0.484555 |
| 7500 | 0.403200 | 0.311247 | 0.483262 |
| 8000 | 0.401500 | 0.295637 | 0.476566 |
| 8500 | 0.397000 | 0.301321 | 0.471672 |
| 9000 | 0.371600 | 0.295639 | 0.468440 |
| 9500 | 0.370700 | 0.294039 | 0.468902 |
| 10000 | 0.364900 | 0.291195 | 0.468440 |
| 10500 | 0.348300 | 0.284898 | 0.461098 |
| 11000 | 0.350100 | 0.281764 | 0.459805 |
| 11500 | 0.336900 | 0.291022 | 0.461606 |
| 12000 | 0.330700 | 0.280467 | 0.455234 |
| 12500 | 0.322500 | 0.271714 | 0.452694 |
| 13000 | 0.307400 | 0.289519 | 0.455465 |
| 13500 | 0.309300 | 0.281922 | 0.451217 |
| 14000 | 0.304800 | 0.271514 | 0.452186 |
| 14500 | 0.288100 | 0.286801 | 0.446830 |
| 15000 | 0.293200 | 0.276309 | 0.445399 |
| 15500 | 0.289800 | 0.287188 | 0.446230 |
| 16000 | 0.274800 | 0.286406 | 0.441243 |
| 16500 | 0.271700 | 0.284754 | 0.441520 |
| 17000 | 0.262500 | 0.275431 | 0.442167 |
| 17500 | 0.255500 | 0.276575 | 0.439858 |
| 18000 | 0.260200 | 0.269911 | 0.435425 |
| 18500 | 0.250600 | 0.270519 | 0.434686 |
| 19000 | 0.243300 | 0.267655 | 0.437826 |
| 19500 | 0.240600 | 0.277109 | 0.431731 |
| 20000 | 0.237200 | 0.266622 | 0.433994 |
| 20500 | 0.231300 | 0.273015 | 0.428868 |
| 21000 | 0.227200 | 0.263024 | 0.430161 |
| 21500 | 0.220400 | 0.272880 | 0.429607 |
| 22000 | 0.218600 | 0.272340 | 0.426883 |
| 22500 | 0.213100 | 0.277066 | 0.428407 |
| 23000 | 0.205000 | 0.278404 | 0.424020 |
| 23500 | 0.200900 | 0.270877 | 0.418987 |
| 24000 | 0.199000 | 0.289120 | 0.425821 |
| 24500 | 0.196100 | 0.275831 | 0.424066 |
| 25000 | 0.191100 | 0.282822 | 0.421850 |
| 25500 | 0.190100 | 0.275820 | 0.418248 |
| 26000 | 0.178800 | 0.279208 | 0.419125 |
| 26500 | 0.183100 | 0.271464 | 0.419218 |
| 27000 | 0.177400 | 0.280869 | 0.419680 |
| 27500 | 0.171800 | 0.279593 | 0.414924 |
| 28000 | 0.172900 | 0.276949 | 0.417648 |
| 28500 | 0.164900 | 0.283491 | 0.417786 |
| 29000 | 0.164800 | 0.283122 | 0.416078 |
| 29500 | 0.165500 | 0.281969 | 0.415801 |
| 30000 | 0.163800 | 0.283319 | 0.412753 |
| 30500 | 0.153500 | 0.285702 | 0.414046 |
| 31000 | 0.156500 | 0.285041 | 0.412615 |
| 31500 | 0.150900 | 0.284336 | 0.413723 |
| 32000 | 0.151800 | 0.285922 | 0.412292 |
| 32500 | 0.149200 | 0.289461 | 0.412153 |
| 33000 | 0.145400 | 0.291322 | 0.409567 |
| 33500 | 0.145600 | 0.294361 | 0.409614 |
| 34000 | 0.144200 | 0.290686 | 0.409059 |
| 34500 | 0.143400 | 0.289474 | 0.409844 |
| 35000 | 0.143500 | 0.290340 | 0.408367 |
| 35500 | 0.143200 | 0.289581 | 0.407351 |
| 36000 | 0.138400 | 0.292782 | 0.408736 |
| 36500 | 0.137900 | 0.289108 | 0.408044 |
| 37000 | 0.138200 | 0.292127 | 0.407166 |
| 37500 | 0.134600 | 0.291797 | 0.408413 |
| 38000 | 0.139800 | 0.290056 | 0.408090 |
| 38500 | 0.136500 | 0.291198 | 0.408090 |
| 39000 | 0.137700 | 0.289696 | 0.408044 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id Akashpb13/Kabyle_xlsr --dataset mozilla-foundation/common_voice_8_0 --config kab --split test
```
|
Akashpb13/xlsr_kurmanji_kurdish | Akashpb13 | 2023-03-25T15:40:45Z | 45 | 12 | transformers | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"kmr",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"ku",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-03-02T23:29:04Z | ---
language:
- kmr
- ku
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- kmr
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/xlsr_kurmanji_kurdish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: kmr
metrics:
- name: Test WER
type: wer
value: 0.33073206986250464
- name: Test CER
type: cer
value: 0.08035244447163924
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: kmr
metrics:
- name: Test WER
type: wer
value: 0.33073206986250464
- name: Test CER
type: cer
value: 0.08035244447163924
---
# Akashpb13/xlsr_kurmanji_kurdish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets):
- Loss: 0.292389
- Wer: 0.388585
## Model description
"facebook/wav2vec2-xls-r-300m" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Kurmanji Kurdish train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0
## Training procedure
For creating the training dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000096
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 16
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 200
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|------|---------------|-----------------|----------|
| 200 | 4.382500 | 3.183725 | 1.000000 |
| 400 | 2.870200 | 0.996664 | 0.781117 |
| 600 | 0.609900 | 0.333755 | 0.445052 |
| 800 | 0.326800 | 0.305729 | 0.403157 |
| 1000 | 0.255000 | 0.290734 | 0.391621 |
| 1200 | 0.226300 | 0.292389 | 0.388585 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.1
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id Akashpb13/xlsr_kurmanji_kurdish --dataset mozilla-foundation/common_voice_8_0 --config kmr --split test
```
|
Sosaka/LLaMa-7B-ggml-4bit-OLD | Sosaka | 2023-03-25T15:35:56Z | 0 | 2 | null | [
"license:other",
"region:us"
]
| null | 2023-03-25T14:15:14Z | ---
license: other
---
!!!This is just repost of https://huggingface.co/hlhr202/llama-7B-ggml-int4 to store it with executable in one repo, so go to the original repo and give him a like
!!!Model in this repo is incompartible with new llama-cpp, use versions above 20-03-2023 |
cleanrl/Phoenix-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T15:30:10Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Phoenix-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T15:30:08Z | ---
tags:
- Phoenix-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Phoenix-v5
type: Phoenix-v5
metrics:
- type: mean_reward
value: 87729.00 +/- 33838.88
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Phoenix-v5**
This is a trained model of a PPO agent playing Phoenix-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --env-id Phoenix-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Phoenix-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Phoenix-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Phoenix-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_impala_atari_wrapper.py --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Phoenix-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Phoenix-v5',
'exp_name': 'cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/MsPacman-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed3 | cleanrl | 2023-03-25T15:27:25Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"MsPacman-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T15:27:23Z | ---
tags:
- MsPacman-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MsPacman-v5
type: MsPacman-v5
metrics:
- type: mean_reward
value: 3826.00 +/- 1373.03
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **MsPacman-v5**
This is a trained model of a PPO agent playing MsPacman-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --env-id MsPacman-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/MsPacman-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/MsPacman-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/MsPacman-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_impala_atari_wrapper.py --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id MsPacman-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'MsPacman-v5',
'exp_name': 'cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/Jamesbond-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T15:26:36Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Jamesbond-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T15:26:35Z | ---
tags:
- Jamesbond-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Jamesbond-v5
type: Jamesbond-v5
metrics:
- type: mean_reward
value: 1595.00 +/- 1566.44
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Jamesbond-v5**
This is a trained model of a PPO agent playing Jamesbond-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --env-id Jamesbond-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Jamesbond-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Jamesbond-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Jamesbond-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_impala_atari_wrapper.py --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Jamesbond-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Jamesbond-v5',
'exp_name': 'cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
cleanrl/MsPacman-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1 | cleanrl | 2023-03-25T15:26:07Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"MsPacman-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T15:26:05Z | ---
tags:
- MsPacman-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MsPacman-v5
type: MsPacman-v5
metrics:
- type: mean_reward
value: 4207.00 +/- 1438.62
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **MsPacman-v5**
This is a trained model of a PPO agent playing MsPacman-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --env-id MsPacman-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/MsPacman-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/MsPacman-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/MsPacman-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_impala_atari_wrapper.py --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id MsPacman-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'MsPacman-v5',
'exp_name': 'cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
droid22/ppo-SnowballTarget | droid22 | 2023-03-25T15:21:42Z | 12 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-03-25T15:21:37Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: droid22/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ys7yoo/kosbert_sts | ys7yoo | 2023-03-25T15:16:50Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-03-25T15:13:44Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 45 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 23,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
zaydzuhri/flan-t5-small-tldr-50k | zaydzuhri | 2023-03-25T15:15:28Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-03-25T10:33:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-small-tldr-50k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-small-tldr-50k
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the Reddit TL;DR dataset (https://zenodo.org/record/1168855#.ZB8P-iFByUk).
It achieves the following results on the evaluation set:
- Gen Len: 16.4422
- Loss: 3.2423
- Rouge1: 14.7049
- Rouge2: 3.2396
- Rougel: 12.5104
- Rougelsum: 12.9681
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.5507 | 1.0 | 5625 | 16.1424 | 3.2752 | 14.2302 | 2.9853 | 12.1734 | 12.5894 |
| 3.4842 | 2.0 | 11250 | 16.1126 | 3.2569 | 14.3966 | 3.0939 | 12.2437 | 12.6705 |
| 3.4288 | 3.0 | 16875 | 16.39 | 3.2481 | 14.6879 | 3.2647 | 12.5199 | 12.9681 |
| 3.4176 | 4.0 | 22500 | 16.2948 | 3.2432 | 14.7198 | 3.2693 | 12.5436 | 12.9885 |
| 3.4033 | 5.0 | 28125 | 16.4422 | 3.2423 | 14.7049 | 3.2396 | 12.5104 | 12.9681 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
danzz06/my_awesome_qa_model | danzz06 | 2023-03-25T15:05:32Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-03-23T16:27:44Z | ---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6395
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 0.5410 |
| 0.4883 | 2.0 | 500 | 0.5437 |
| 0.4883 | 3.0 | 750 | 0.5913 |
| 0.226 | 4.0 | 1000 | 0.7683 |
| 0.226 | 5.0 | 1250 | 0.8280 |
| 0.1266 | 6.0 | 1500 | 0.8528 |
| 0.1266 | 7.0 | 1750 | 0.9454 |
| 0.0868 | 8.0 | 2000 | 1.1004 |
| 0.0868 | 9.0 | 2250 | 1.2183 |
| 0.0608 | 10.0 | 2500 | 1.2702 |
| 0.0608 | 11.0 | 2750 | 1.3823 |
| 0.0427 | 12.0 | 3000 | 1.4355 |
| 0.0427 | 13.0 | 3250 | 1.4961 |
| 0.0318 | 14.0 | 3500 | 1.6042 |
| 0.0318 | 15.0 | 3750 | 1.6052 |
| 0.0271 | 16.0 | 4000 | 1.5435 |
| 0.0271 | 17.0 | 4250 | 1.6205 |
| 0.0215 | 18.0 | 4500 | 1.6248 |
| 0.0215 | 19.0 | 4750 | 1.6113 |
| 0.0157 | 20.0 | 5000 | 1.6395 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
cleanrl/DemonAttack-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2 | cleanrl | 2023-03-25T15:01:02Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"DemonAttack-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-25T15:01:00Z | ---
tags:
- DemonAttack-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: DemonAttack-v5
type: DemonAttack-v5
metrics:
- type: mean_reward
value: 131623.00 +/- 1986.93
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **DemonAttack-v5**
This is a trained model of a PPO agent playing DemonAttack-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --env-id DemonAttack-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/DemonAttack-v5-cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_impala_atari_wrapper.py --exp-name cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id DemonAttack-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'DemonAttack-v5',
'exp_name': 'cleanba_impala_envpool_impala_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
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