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 +1,80 @@
+---
+library_name: stable-baselines3
+tags:
+- InvertedPendulum-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: InvertedPendulum-v2
+      type: InvertedPendulum-v2
+    metrics:
+    - type: mean_reward
+      value: 1000.00 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **InvertedPendulum-v2**
+This is a trained model of a **PPO** agent playing **InvertedPendulum-v2**
+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 ppo --env InvertedPendulum-v2 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo ppo --env InvertedPendulum-v2  -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 ppo --env InvertedPendulum-v2 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo ppo --env InvertedPendulum-v2  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo ppo --env InvertedPendulum-v2 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo ppo --env InvertedPendulum-v2 -f logs/ -orga qgallouedec
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 64),
+             ('clip_range', 0.4),
+             ('ent_coef', 1.37976e-07),
+             ('gae_lambda', 0.9),
+             ('gamma', 0.999),
+             ('learning_rate', 0.000222425),
+             ('max_grad_norm', 0.3),
+             ('n_envs', 1),
+             ('n_epochs', 5),
+             ('n_steps', 32),
+             ('n_timesteps', 1000000.0),
+             ('normalize', True),
+             ('policy', 'MlpPolicy'),
+             ('vf_coef', 0.19816),
+             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
+```

args.yml

@@ -0,0 +1,83 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - ppo
+  - - conf_file
+    - null
+  - - device
+    - auto
+  - - env
+    - InvertedPendulum-v2
+  - - 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
+    - 1058914254
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - runs/InvertedPendulum-v2__ppo__1058914254__1675811206
+  - - track
+    - true
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1
+  - - wandb_entity
+    - openrlbenchmark
+  - - wandb_project_name
+    - sb3
+  - - wandb_tags
+    - []
+  - - yaml_file
+    - null

config.yml

@@ -0,0 +1,29 @@
+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+    - 64
+  - - clip_range
+    - 0.4
+  - - ent_coef
+    - 1.37976e-07
+  - - gae_lambda
+    - 0.9
+  - - gamma
+    - 0.999
+  - - learning_rate
+    - 0.000222425
+  - - max_grad_norm
+    - 0.3
+  - - n_envs
+    - 1
+  - - n_epochs
+    - 5
+  - - n_steps
+    - 32
+  - - n_timesteps
+    - 1000000.0
+  - - normalize
+    - true
+  - - policy
+    - MlpPolicy
+  - - vf_coef
+    - 0.19816

env_kwargs.yml

@@ -0,0 +1 @@
+{}

ppo-InvertedPendulum-v2.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:92cf2e19ec6984e1c35d052bd757f2a6598fe6f5681db7fe47669db92d16600a
+size 143251

ppo-InvertedPendulum-v2/_stable_baselines3_version

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+1.8.0a6

ppo-InvertedPendulum-v2/data

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+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fe4fa012ee0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe4fa012f70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe4fa014040>",
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+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe4fa014280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe4fa014310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fe4fa0143a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe4fa014430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe4fa0144c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe4fa014550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fe4fa013f00>"
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+    "verbose": 1,
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+        "low": "[-inf -inf -inf -inf]",
+        "high": "[inf inf inf inf]",
+        "bounded_below": "[False False False False]",
+        "bounded_above": "[False False False False]",
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+    "action_space": {
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