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: 1403.72 +/- 136.83
+      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 sha256:050471b630d10f78b92418f65fef40f96e167b59b446a713709fd1cf086eacdd
+size 129248

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,107 @@
+{
+    "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 0x7f7ef0998040>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7ef09980d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7ef0998160>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7ef09981f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f7ef0998280>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f7ef0998310>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7ef09983a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7ef0998430>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f7ef09984c0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7ef0998550>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7ef09985e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7ef0998670>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f7ef0995cc0>"
+    },
+    "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
+        }
+    },
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1686311684442503639,
+    "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,
+    "_stats_window_size": 100,
+    "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,
+    "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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4da537ce1a8a9e373cf25dff4297133a934233587a7da76185b927adf5fdffa7
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:848aa99ba543c22534037dd73c1a431a08f3690b7c616d61107b0a4a683943a9
+size 56894

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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- 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 0x7f7ef0998040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7ef09980d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7ef0998160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7ef09981f0>", "_build": "<function ActorCriticPolicy._build at 0x7f7ef0998280>", "forward": "<function ActorCriticPolicy.forward at 0x7f7ef0998310>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7ef09983a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7ef0998430>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7ef09984c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7ef0998550>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7ef09985e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7ef0998670>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f7ef0995cc0>"}, "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}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1686311684442503639, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9PdRBNVR1phZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_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, "_stats_window_size": 100, "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, "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, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "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:ba7f936f61902e4fc3daa31aed07c93017981950bc77a3b50959e79b9f453e3c
+size 1087181

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1403.7240223313916, "std_reward": 136.82958508546176, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-09T13:02:29.235735"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2df780037069751264ac5055b5d40102253270998cf82a5a5d72f9843e71ac3c
+size 2176