Initial commit

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: 1173.67 +/- 31.33
+      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 sha256:65c22a4774df5eb8e79d345ad42139fcf652586c3e8fc628ff61eba2dd507705
+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 0x7f9d6c0680d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9d6c068160>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9d6c0681f0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9d6c068280>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f9d6c068310>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f9d6c0683a0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9d6c068430>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9d6c0684c0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f9d6c068550>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9d6c0685e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9d6c068670>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9d6c068700>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f9d6c069440>"
+    },
+    "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": 1682124011473069591,
+    "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 sha256:44f611567bb0a0086ddc9eb8e55acf369f14425bc6ae03f93eb0ff5f3857be6d
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba312015de28c1e9bbaaa7e2d0c6c6f17b3aca43f061a4446edeb22fac076cc0
+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 0x7f9d6c0680d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9d6c068160>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9d6c0681f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9d6c068280>", "_build": "<function ActorCriticPolicy._build at 0x7f9d6c068310>", "forward": "<function ActorCriticPolicy.forward at 0x7f9d6c0683a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9d6c068430>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9d6c0684c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9d6c068550>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9d6c0685e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9d6c068670>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9d6c068700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9d6c069440>"}, "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": 1682124011473069591, "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:": "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, "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"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1173.6733787774806, "std_reward": 31.32930266488067, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-22T01:41:43.963111"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e10f6359944b1075539fd057511200589b091a8d036a2b5557189c4ac6b94f3c
+size 2170