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:
+- Swimmer-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Swimmer-v3
+      type: Swimmer-v3
+    metrics:
+    - type: mean_reward
+      value: 334.43 +/- 2.11
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **Swimmer-v3**
+This is a trained model of a **PPO** agent playing **Swimmer-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
+
+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 Swimmer-v3 -orga croumegous -f logs/
+python -m rl_zoo3.enjoy --algo ppo --env Swimmer-v3  -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 Swimmer-v3 -orga croumegous -f logs/
+python -m rl_zoo3.enjoy --algo ppo --env Swimmer-v3  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo ppo --env Swimmer-v3 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo ppo --env Swimmer-v3 -f logs/ -orga croumegous
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 256),
+             ('gae_lambda', 0.98),
+             ('gamma', 0.9999),
+             ('learning_rate', 0.0006),
+             ('n_envs', 4),
+             ('n_steps', 1024),
+             ('n_timesteps', 1000000.0),
+             ('normalize', True),
+             ('policy', 'MlpPolicy'),
+             ('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
+    - Swimmer-v3
+  - - env_kwargs
+    - null
+  - - eval_episodes
+    - 5
+  - - eval_freq
+    - 25000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - null
+  - - log_folder
+    - logs/
+  - - log_interval
+    - -1
+  - - max_total_trials
+    - null
+  - - n_eval_envs
+    - 1
+  - - 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
+    - 2355280636
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - ''
+  - - track
+    - false
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1
+  - - wandb_entity
+    - null
+  - - wandb_project_name
+    - sb3
+  - - wandb_tags
+    - []
+  - - yaml_file
+    - null

config.yml

@@ -0,0 +1,19 @@
+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+    - 256
+  - - gae_lambda
+    - 0.98
+  - - gamma
+    - 0.9999
+  - - learning_rate
+    - 0.0006
+  - - n_envs
+    - 4
+  - - n_steps
+    - 1024
+  - - n_timesteps
+    - 1000000.0
+  - - normalize
+    - true
+  - - policy
+    - MlpPolicy

env_kwargs.yml

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

ppo-Swimmer-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef047b7f92d64cf54ebcdca780950bedb06929c52e9efb766afc2f23f04de9c8
+size 151697

ppo-Swimmer-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-Swimmer-v3/data

@@ -0,0 +1,103 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 0x7f92c4f3d670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f92c4f3d700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f92c4f3d790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f92c4f3d820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f92c4f3d8b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f92c4f3d940>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f92c4f3d9d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f92c4f3da60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f92c4f3daf0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f92c4f3db80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f92c4f3dc10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f92c4f3dca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f92c4f3f090>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float64",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            2
+        ],
+        "low": "[-1. -1.]",
+        "high": "[1. 1.]",
+        "bounded_below": "[ True  True]",
+        "bounded_above": "[ True  True]",
+        "_np_random": "RandomState(MT19937)"
+    },
+    "n_envs": 1,
+    "num_timesteps": 1003520,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": 0,
+    "action_noise": null,
+    "start_time": 1673941688820036680,
+    "learning_rate": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.0035199999999999676,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 2450,
+    "n_steps": 1024,
+    "gamma": 0.9999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 256,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-Swimmer-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fafc033c16dfef36a0727730d241f84b61653680be9b72494155bbc2fd154b27
+size 87280

ppo-Swimmer-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:45d9bdb4bc517a64995be10509b427b7c1f7e2d0b6588bf1bcb9d9b3604aaccd
+size 43070

ppo-Swimmer-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-Swimmer-v3/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.4.0-137-generic-x86_64-with-glibc2.29 # 154-Ubuntu SMP Thu Jan 5 17:03:22 UTC 2023
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: False
+- Numpy: 1.21.1
+- Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a828494dfa62c3e6b70a835c0f66be62b7c393516f5d94558bac0a17c9b9f60f
+size 1410371

results.json

@@ -0,0 +1 @@
+{"mean_reward": 334.43160509999996, "std_reward": 2.1119781167335776, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-17T09:06:29.270716"}

train_eval_metrics.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4038f304849191fa926164e6b4a89b9d3c285ee2edd042383ddb0f478f373f39
+size 32603

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:884df7d27d417177764ec9bc608c7abdceb7af32589d5195c14ba509152a9708
+size 4517