Initial commit

.gitattributes

@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+vec_normalize.pkl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,58 @@
+---
+library_name: stable-baselines3
+tags:
+- Ant-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+ results:
+ - metrics:
+ - type: mean_reward
+ value: 1480.47 +/- 407.39
+ name: mean_reward
+ task:
+ type: reinforcement-learning
+ name: reinforcement-learning
+ dataset:
+ name: Ant-v3
+ type: Ant-v3
+---
+
+# **PPO** Agent playing **Ant-v3**
+This is a trained model of a **PPO** agent playing **Ant-v3**
+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
+
+```
+# Download model and save it into the logs/ folder
+python -m utils.load_from_hub --algo ppo --env Ant-v3 -orga sb3 -f logs/
+python enjoy.py --algo ppo --env Ant-v3 -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python train.py --algo ppo --env Ant-v3 -f logs/
+# Upload the model and generate video (when possible)
+python -m utils.push_to_hub --algo ppo --env Ant-v3 -f logs/ -orga sb3
+```
+
+## Hyperparameters
+```python
+OrderedDict([('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
+ ('n_timesteps', 1000000.0),
+ ('normalize', True),
+ ('policy', 'MlpPolicy'),
+ ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
+```

args.yml

@@ -0,0 +1,65 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+ - ppo
+ - - env
+ - Ant-v3
+ - - env_kwargs
+ - null
+ - - eval_episodes
+ - 20
+ - - eval_freq
+ - 10000
+ - - gym_packages
+ - []
+ - - hyperparams
+ - null
+ - - log_folder
+ - logs/
+ - - log_interval
+ - 10
+ - - n_eval_envs
+ - 5
+ - - n_evaluations
+ - 20
+ - - n_jobs
+ - 1
+ - - n_startup_trials
+ - 10
+ - - n_timesteps
+ - -1
+ - - n_trials
+ - 10
+ - - no_optim_plots
+ - false
+ - - num_threads
+ - 2
+ - - optimization_log_path
+ - null
+ - - optimize_hyperparameters
+ - false
+ - - pruner
+ - median
+ - - sampler
+ - tpe
+ - - save_freq
+ - -1
+ - - save_replay_buffer
+ - false
+ - - seed
+ - 594371
+ - - storage
+ - null
+ - - study_name
+ - null
+ - - tensorboard_log
+ - ''
+ - - trained_agent
+ - ''
+ - - truncate_last_trajectory
+ - true
+ - - uuid
+ - true
+ - - vec_env
+ - dummy
+ - - verbose
+ - 1

config.yml

@@ -0,0 +1,9 @@
+!!python/object/apply:collections.OrderedDict
+- - - env_wrapper
+ - sb3_contrib.common.wrappers.TimeFeatureWrapper
+ - - n_timesteps
+ - 1000000.0
+ - - normalize
+ - true
+ - - policy
+ - MlpPolicy

env_kwargs.yml

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

ppo-Ant-v3.zip

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+oid sha256:16a7f67bb735a6c2f230d8fa008e2cfa56750b52d03c1bc02924e18db4afb1c6
+size 315831

ppo-Ant-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.1a8

ppo-Ant-v3/data

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+{
+ "policy_class": {
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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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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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