Upload PPO BipedalWalker-v3 trained agent

.gitattributes

@@ -25,3 +25,4 @@ 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

README.md

@@ -0,0 +1,28 @@
+---
+library_name: stable-baselines3
+tags:
+- BipedalWalker-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 292.97 +/- 1.66
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: BipedalWalker-v3
+      type: BipedalWalker-v3
+---
+
+  # **PPO** Agent playing **BipedalWalker-v3**
+  This is a trained model of a **PPO** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+  
+  ## Usage (with Stable-baselines3)
+  TODO: Add your code
+  

config.json

@@ -0,0 +1 @@
+{"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 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    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f7996fc5b00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7996fc5b90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7996fc5c20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7996fc5cb0>", "_build": "<function ActorCriticPolicy._build at 0x7f7996fc5d40>", "forward": "<function ActorCriticPolicy.forward at 0x7f7996fc5dd0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7996fc5e60>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7996fc5ef0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7996fc5f80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7996fcb050>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7996fcb0e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7996fa1270>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [24], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVfQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLBIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAIC/AACAvwAAgL8AAIC/lGgLSwSFlIwBQ5R0lFKUjARoaWdolGgTKJYQAAAAAAAAAAAAgD8AAIA/AACAPwAAgD+UaAtLBIWUaBZ0lFKUjA1ib3VuZGVkX2JlbG93lGgTKJYEAAAAAAAAAAEBAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLBIWUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYEAAAAAAAAAAEBAQGUaCJLBIWUaBZ0lFKUjApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "_shape": [4], "low": "[-1. -1. -1. -1.]", "high": "[1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True]", "bounded_above": "[ True  True  True  True]", "_np_random": null}, "n_envs": 32, "num_timesteps": 256000, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652367884.002565, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.016959999999999975, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 340, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-bipedal-walker-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3e70ccb3f1ed59801dc1b25b0d0637e96c8382ff2a28e63de5fbd1ac30920b0
+size 174009

ppo-bipedal-walker-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

ppo-bipedal-walker-v3/data

@@ -0,0 +1,99 @@
+{
+    "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 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    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f7996fc5b00>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7996fc5b90>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7996fc5c20>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7996fc5cb0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f7996fc5d40>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f7996fc5dd0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7996fc5e60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f7996fc5ef0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7996fc5f80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7996fcb050>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7996fcb0e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f7996fa1270>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            24
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False False False False False\n False False False False 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": [
+            4
+        ],
+        "low": "[-1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True]",
+        "bounded_above": "[ True  True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 32,
+    "num_timesteps": 256000,
+    "_total_timesteps": 200000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652367884.002565,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.016959999999999975,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 340,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-bipedal-walker-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:efcaaaf94a1c91206014ad93294161e2d96e76bc735af39ecee04e0b5f77f41f
+size 101783

ppo-bipedal-walker-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65a1ec9ae54623427fdd5c26cce44cdfd474c69324d4fbb8eb2d6c062074026d
+size 51710

ppo-bipedal-walker-v3/pytorch_variables.pth

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

ppo-bipedal-walker-v3/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3db5be0978d89076fc5953941983eaff9a1f6e6d1292a414ca769081e231a455
+size 424071

results.json

@@ -0,0 +1 @@
+{"mean_reward": 292.96971325705425, "std_reward": 1.6576810567920495, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-12T15:45:20.685868"}