Initial commit

.gitattributes

@@ -33,3 +33,4 @@ 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: 1299.45 +/- 236.14
+      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:eb1467ed2d07406a37fcda1e83bec1726302bc79100183a65f2c0dd4cc8083a2
+size 129266

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 0x793ed5239d80>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x793ed5239e10>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x793ed5239ea0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x793ed5239f30>",
+        "_build": "<function ActorCriticPolicy._build at 0x793ed5239fc0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x793ed523a050>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x793ed523a0e0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x793ed523a170>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x793ed523a200>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x793ed523a290>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x793ed523a320>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x793ed523a3b0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x793ed5236e00>"
+    },
+    "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": 2000012,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1691185333955875642,
+    "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": -5.999999999950489e-06,
+    "_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": 71429,
+    "n_steps": 7,
+    "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 sha256:252c2b59bd14577049e77cf6ef9b719f7bf050c7e90c6ab61ec0ec8be9eac438
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:461c3483be8e7de99eabd7cee94b702cb8f7405f7b6cd980be3a22aa55206631
+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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 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 0x793ed5239d80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x793ed5239e10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x793ed5239ea0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x793ed5239f30>", "_build": "<function ActorCriticPolicy._build at 0x793ed5239fc0>", "forward": "<function ActorCriticPolicy.forward at 0x793ed523a050>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x793ed523a0e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x793ed523a170>", "_predict": "<function ActorCriticPolicy._predict at 0x793ed523a200>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x793ed523a290>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x793ed523a320>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x793ed523a3b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x793ed5236e00>"}, "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": 2000012, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1691185333955875642, "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": -5.999999999950489e-06, "_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": 71429, "n_steps": 7, "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 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 sha256:879136af9bf8480d6777c9725234b458a6191b6c7485b51cdac1266037ce06f2
+size 1062916

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1299.4477577617859, "std_reward": 236.13927981998918, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-04T22:43:43.142193"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c6e73b73ccc1240ae4ff3486b81b085297419907c40c86da248da8b35df295f
+size 2176