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: 2193.35 +/- 129.01
+      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:bef364df87028559b6664e4bb224d0e93df05b911ed06cddc4132dfd892b0f90
+size 129265

a2c-AntBulletEnv-v0/_stable_baselines3_version

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

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,106 @@
+{
+    "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 0x7f72c4a9c9d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f72c4a9ca60>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f72c4a9caf0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f72c4a9cb80>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f72c4a9cc10>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f72c4a9cca0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f72c4a9cd30>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f72c4a9cdc0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f72c4a9ce50>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f72c4a9cee0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f72c4a9cf70>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f72c4ab6040>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f72c4ab3680>"
+    },
+    "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:": "gAWVZwIAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLHIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWcAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/lGgKSxyFlIwBQ5R0lFKUjARoaWdolGgSKJZwAAAAAAAAAAAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH+UaApLHIWUaBV0lFKUjA1ib3VuZGVkX2JlbG93lGgSKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLHIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaCFLHIWUaBV0lFKUjApfbnBfcmFuZG9tlE51Yi4=",
+        "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": 1200000,
+    "_total_timesteps": 1200000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1679991414170353022,
+    "learning_rate": 0.00096,
+    "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:": "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": 37500,
+    "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 sha256:71a45a2da40bcb560c3c37f39d00d22cb125ca35452713aa919498a2a430a5ef
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4058e4419ccb8152bf9459d6b1a5dd5776c99a0d752d0608a30b7ac7f7040a2
+size 56958

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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- 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 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 0x7f72c4a9c9d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f72c4a9ca60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f72c4a9caf0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f72c4a9cb80>", "_build": "<function ActorCriticPolicy._build at 0x7f72c4a9cc10>", "forward": "<function ActorCriticPolicy.forward at 0x7f72c4a9cca0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f72c4a9cd30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f72c4a9cdc0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f72c4a9ce50>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f72c4a9cee0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f72c4a9cf70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f72c4ab6040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f72c4ab3680>"}, "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": 1200000, "_total_timesteps": 1200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679991414170353022, "learning_rate": 0.00096, "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:": "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": 37500, "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9953e16374a6afb431e3fb1d4abf682cdce13ada25b05ed2a12ddab56fc7115d
+size 1272081

results.json

@@ -0,0 +1 @@
+{"mean_reward": 2193.3515314778315, "std_reward": 129.0116993593632, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-28T08:54:20.131306"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:837a06dc107fc2f3f3945ee9972ad787219fc5447e761ad4f3ff25d4cb1b152e
+size 2136