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
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 1669.48 +/- 188.31
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **AntBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

a2c-AntBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3302fcfa8fc4a4fa16d1463478e3517b29d8cfe5417f76c5442b377bcedc8751
+size 129065

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,105 @@
+{
+    "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 0x7fcf121c9f70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcf121c6040>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcf121c60d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcf121c6160>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fcf121c61f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fcf121c6280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcf121c6310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fcf121c63a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcf121c6430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcf121c64c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcf121c6550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fcf121c2ed0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            28
+        ],
+        "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 -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 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\n 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\n False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1672662491144352912,
+    "learning_rate": 0.00096,
+    "tensorboard_log": "./tensorboard",
+    "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:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_episode_num": 0,
+    "use_sde": true,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 62500,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b0930eb2c4a7765e0fe8ca0a1bbeb25961e2b6d6fcaaa893d788cab8c2bd6dfb
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c60b53fb7393d68b9e50851695bb657d2e23c02592f279fbfec6f48d5620980
+size 56766

a2c-AntBulletEnv-v0/pytorch_variables.pth

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

a2c-AntBulletEnv-v0/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

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 0x7fcf121c9f70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcf121c6040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcf121c60d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcf121c6160>", "_build": "<function ActorCriticPolicy._build at 0x7fcf121c61f0>", "forward": "<function ActorCriticPolicy.forward at 0x7fcf121c6280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcf121c6310>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcf121c63a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcf121c6430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcf121c64c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcf121c6550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fcf121c2ed0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "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 -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 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\n 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\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672662491144352912, "learning_rate": 0.00096, "tensorboard_log": "./tensorboard", "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:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "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 sha256:648ee5cdf2d8e5cd6e11066e6d94ab7bbe7386fa5ff28cfbad9732b02ff4569d
+size 1033337

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1669.4837663263083, "std_reward": 188.31214003855854, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-02T13:25:53.013661"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4da618be8a853be9b1f533231d9b11b7f240753108a3d8f8a22d0fa7024401bf
+size 2521