Initial commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,75 @@
+---
+library_name: stable-baselines3
+tags:
+- AcrobotSwingupSparseDMC-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DDPG
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AcrobotSwingupSparseDMC-v0
+      type: AcrobotSwingupSparseDMC-v0
+    metrics:
+    - type: mean_reward
+      value: 7.10 +/- 14.29
+      name: mean_reward
+      verified: false
+---
+
+# **DDPG** Agent playing **AcrobotSwingupSparseDMC-v0**
+This is a trained model of a **DDPG** agent playing **AcrobotSwingupSparseDMC-v0**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
+and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
+
+The RL Zoo is a training framework for Stable Baselines3
+reinforcement learning agents,
+with hyperparameter optimization and pre-trained agents included.
+
+## Usage (with SB3 RL Zoo)
+
+RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
+SB3: https://github.com/DLR-RM/stable-baselines3<br/>
+SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
+
+Install the RL Zoo (with SB3 and SB3-Contrib):
+```bash
+pip install rl_zoo3
+```
+
+```
+# Download model and save it into the logs/ folder
+python -m rl_zoo3.load_from_hub --algo ddpg --env AcrobotSwingupSparseDMC-v0 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo ddpg --env AcrobotSwingupSparseDMC-v0  -f logs/
+```
+
+If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
+```
+python -m rl_zoo3.load_from_hub --algo ddpg --env AcrobotSwingupSparseDMC-v0 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo ddpg --env AcrobotSwingupSparseDMC-v0  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo ddpg --env AcrobotSwingupSparseDMC-v0 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo ddpg --env AcrobotSwingupSparseDMC-v0 -f logs/ -orga qgallouedec
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 64),
+             ('gamma', 0.99),
+             ('learning_rate', 0.0001),
+             ('n_timesteps', 1000000.0),
+             ('noise_std', 0.3),
+             ('noise_type', 'ornstein-uhlenbeck'),
+             ('policy', 'MlpPolicy'),
+             ('policy_kwargs',
+              'dict(net_arch=dict(pi=[300, 200], qf=[400, 300]))'),
+             ('normalize', False)])
+```

args.yml

@@ -0,0 +1,83 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - ddpg
+  - - conf_file
+    - null
+  - - device
+    - auto
+  - - env
+    - AcrobotSwingupSparseDMC-v0
+  - - env_kwargs
+    - null
+  - - eval_episodes
+    - 20
+  - - eval_freq
+    - 25000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - null
+  - - log_folder
+    - logs
+  - - log_interval
+    - -1
+  - - max_total_trials
+    - null
+  - - n_eval_envs
+    - 5
+  - - n_evaluations
+    - null
+  - - n_jobs
+    - 1
+  - - n_startup_trials
+    - 10
+  - - n_timesteps
+    - -1
+  - - n_trials
+    - 500
+  - - no_optim_plots
+    - false
+  - - num_threads
+    - -1
+  - - optimization_log_path
+    - null
+  - - optimize_hyperparameters
+    - false
+  - - progress
+    - false
+  - - pruner
+    - median
+  - - sampler
+    - tpe
+  - - save_freq
+    - -1
+  - - save_replay_buffer
+    - false
+  - - seed
+    - 3376376922
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - runs/AcrobotSwingupSparseDMC-v0__ddpg__3376376922__1673811015
+  - - track
+    - true
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1
+  - - wandb_entity
+    - qgallouedec
+  - - wandb_project_name
+    - dmc
+  - - wandb_tags
+    - []
+  - - yaml_file
+    - null

config.yml

@@ -0,0 +1,17 @@
+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+    - 64
+  - - gamma
+    - 0.99
+  - - learning_rate
+    - 0.0001
+  - - n_timesteps
+    - 1000000.0
+  - - noise_std
+    - 0.3
+  - - noise_type
+    - ornstein-uhlenbeck
+  - - policy
+    - MlpPolicy
+  - - policy_kwargs
+    - dict(net_arch=dict(pi=[300, 200], qf=[400, 300]))

ddpg-AcrobotSwingupSparseDMC-v0.zip

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ddpg-AcrobotSwingupSparseDMC-v0/_stable_baselines3_version

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+1.7.0

ddpg-AcrobotSwingupSparseDMC-v0/actor.optimizer.pth

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ddpg-AcrobotSwingupSparseDMC-v0/critic.optimizer.pth

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ddpg-AcrobotSwingupSparseDMC-v0/data

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+        "__doc__": "\n    Policy class (with both actor and critic) for TD3.\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    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
+        "__init__": "<function TD3Policy.__init__ at 0x149a98280>",
+        "_build": "<function TD3Policy._build at 0x149a98310>",
+        "_get_constructor_parameters": "<function TD3Policy._get_constructor_parameters at 0x149a983a0>",
+        "make_actor": "<function TD3Policy.make_actor at 0x149a98430>",
+        "make_critic": "<function TD3Policy.make_critic at 0x149a984c0>",
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+    },
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+    "policy_kwargs": {
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+            "qf": [
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+    "target_noise_clip": 0.0,
+    "target_policy_noise": 0.1,
+    "actor_batch_norm_stats": [],
+    "critic_batch_norm_stats": [],
+    "actor_batch_norm_stats_target": [],
+    "critic_batch_norm_stats_target": []
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ddpg-AcrobotSwingupSparseDMC-v0/system_info.txt

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+- Python: 3.10.9
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1
+- GPU Enabled: False
+- Numpy: 1.24.1
+- Gym: 0.21.0

env_kwargs.yml

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replay.mp4

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results.json

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