Upload DQN LunarLander-v2 trained agent
Browse files- DQN-LUNARLANDER-V2.zip +3 -0
- DQN-LUNARLANDER-V2/_stable_baselines3_version +1 -0
- DQN-LUNARLANDER-V2/data +114 -0
- DQN-LUNARLANDER-V2/policy.optimizer.pth +3 -0
- DQN-LUNARLANDER-V2/policy.pth +3 -0
- DQN-LUNARLANDER-V2/pytorch_variables.pth +3 -0
- DQN-LUNARLANDER-V2/system_info.txt +7 -0
- README.md +37 -0
- config.json +1 -0
- results.json +1 -0
DQN-LUNARLANDER-V2.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:8ad2a1b63e51c9ccc54737090d165ce1f493f88cf99b1ebe7cd06921edf10812
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size 108829
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DQN-LUNARLANDER-V2/_stable_baselines3_version
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1.7.0
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DQN-LUNARLANDER-V2/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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"__module__": "stable_baselines3.dqn.policies",
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"__doc__": "\n Policy class with Q-Value Net and target net for DQN\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
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"__init__": "<function DQNPolicy.__init__ at 0x000001C455B78040>",
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"_build": "<function DQNPolicy._build at 0x000001C455B780D0>",
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"make_q_net": "<function DQNPolicy.make_q_net at 0x000001C455B78160>",
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"forward": "<function DQNPolicy.forward at 0x000001C455B781F0>",
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"_predict": "<function DQNPolicy._predict at 0x000001C455B78280>",
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"_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x000001C455B78310>",
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"set_training_mode": "<function DQNPolicy.set_training_mode at 0x000001C455B783A0>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x000001C455B77840>"
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},
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"verbose": 1,
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"policy_kwargs": {},
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"__doc__": "\n Replay buffer used in off-policy algorithms like SAC/TD3.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n of the replay buffer which reduces by almost a factor two the memory used,\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n Cannot be used in combination with handle_timeout_termination.\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
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"__init__": "<function ReplayBuffer.__init__ at 0x000001C455B3B790>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x000001C455B3C940>"
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},
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"replay_buffer_kwargs": {},
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"train_freq": {
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":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
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},
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"actor": null,
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"use_sde_at_warmup": false,
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"exploration_initial_eps": 1.0,
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"exploration_final_eps": 0.05,
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"exploration_fraction": 0.1,
|
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"target_update_interval": 10000,
|
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"_n_calls": 200000,
|
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"max_grad_norm": 10,
|
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"exploration_rate": 0.05,
|
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"exploration_schedule": {
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":type:": "<class 'function'>",
|
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":serialized:": "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"
|
111 |
+
},
|
112 |
+
"batch_norm_stats": [],
|
113 |
+
"batch_norm_stats_target": []
|
114 |
+
}
|
DQN-LUNARLANDER-V2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1cbe418691f8d86d4425191319a9ea19ea611c0bd7798341d9b2c7c3c00c7750
|
3 |
+
size 44975
|
DQN-LUNARLANDER-V2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:966c53da307947814e87a17206d40c13c19c379fca6a8be4cd335ca88ef5e8ea
|
3 |
+
size 44033
|
DQN-LUNARLANDER-V2/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
3 |
+
size 431
|
DQN-LUNARLANDER-V2/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Windows-10-10.0.22621-SP0 10.0.22621
|
2 |
+
- Python: 3.9.13
|
3 |
+
- Stable-Baselines3: 1.7.0
|
4 |
+
- PyTorch: 1.13.1
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.21.5
|
7 |
+
- Gym: 0.21.0
|
README.md
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- LunarLander-v2
|
5 |
+
- deep-reinforcement-learning
|
6 |
+
- reinforcement-learning
|
7 |
+
- stable-baselines3
|
8 |
+
model-index:
|
9 |
+
- name: DQN
|
10 |
+
results:
|
11 |
+
- task:
|
12 |
+
type: reinforcement-learning
|
13 |
+
name: reinforcement-learning
|
14 |
+
dataset:
|
15 |
+
name: LunarLander-v2
|
16 |
+
type: LunarLander-v2
|
17 |
+
metrics:
|
18 |
+
- type: mean_reward
|
19 |
+
value: -143.52 +/- 17.92
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **DQN** Agent playing **LunarLander-v2**
|
25 |
+
This is a trained model of a **DQN** agent playing **LunarLander-v2**
|
26 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
27 |
+
|
28 |
+
## Usage (with Stable-baselines3)
|
29 |
+
TODO: Add your code
|
30 |
+
|
31 |
+
|
32 |
+
```python
|
33 |
+
from stable_baselines3 import ...
|
34 |
+
from huggingface_sb3 import load_from_hub
|
35 |
+
|
36 |
+
...
|
37 |
+
```
|
config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
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results.json
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@@ -0,0 +1 @@
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{"mean_reward": -143.51571277751427, "std_reward": 17.917075807970136, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-02T10:09:40.073361"}
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