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: 2090.94 +/- 510.35
+      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 sha256:292326f84a0da294220a9bdc2cdab8681044b241c8994b41f379d8329805165d
+size 129231

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 0x7fdc3eece430>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdc3eece4c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdc3eece550>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdc3eece5e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fdc3eece670>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fdc3eece700>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fdc3eece790>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdc3eece820>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fdc3eece8b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdc3eece940>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdc3eece9d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdc3eecea60>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fdc3eecdd40>"
+    },
+    "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": 2400000,
+    "_total_timesteps": 2400000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1681054061807002522,
+    "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": 75000,
+    "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 sha256:4423838673101d95c1d7d68fbd061254d58708a08bc4b17a79e0070f903def4d
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b03b90c77f0415d3d8d0c3481d46140c893fc72797e634e0cc62c3f814889f45
+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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.0+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 0x7fdc3eece430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdc3eece4c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdc3eece550>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdc3eece5e0>", "_build": "<function ActorCriticPolicy._build at 0x7fdc3eece670>", "forward": "<function ActorCriticPolicy.forward at 0x7fdc3eece700>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fdc3eece790>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdc3eece820>", "_predict": "<function ActorCriticPolicy._predict at 0x7fdc3eece8b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdc3eece940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdc3eece9d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdc3eecea60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fdc3eecdd40>"}, "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": 2400000, "_total_timesteps": 2400000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1681054061807002522, "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": 75000, "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+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:fbd2e5541487713518b1dedc0d61482ec69710a4e5901644263fd0d7d2bf22f4
+size 1272877

results.json

@@ -0,0 +1 @@
+{"mean_reward": 2090.939521356556, "std_reward": 510.3478797539977, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-09T16:41:31.941910"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43e60a8352388358b6f5878708145a0e1a550f3128b13bcd582785a00a63bbf4
+size 2170