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: 821.22 +/- 78.64
+      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 sha256:525e3a86c4e06dda3d02fb4dd39ab98ddaf7a749fc1e3372fc0c01934b64ad99
+size 129260

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 0x7fdb60f62160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdb60f621f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdb60f62280>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdb60f62310>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fdb60f623a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fdb60f62430>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fdb60f624c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdb60f62550>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fdb60f625e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdb60f62670>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdb60f62700>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdb60f62790>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fdb60f61120>"
+    },
+    "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": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1677162603848932013,
+    "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": 31250,
+    "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 sha256:40860c03f43c19230cb515f2f95365f383a2465cc40806bc5d3560302edb117f
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:623fed0706abdf66eb0bccf13c8a8b0f42ff725f44060a54083aef688890b1e1
+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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- 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 0x7fdb60f62160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdb60f621f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdb60f62280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdb60f62310>", "_build": "<function ActorCriticPolicy._build at 0x7fdb60f623a0>", "forward": "<function ActorCriticPolicy.forward at 0x7fdb60f62430>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fdb60f624c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdb60f62550>", "_predict": "<function ActorCriticPolicy._predict at 0x7fdb60f625e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdb60f62670>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdb60f62700>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdb60f62790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fdb60f61120>"}, "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": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677162603848932013, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAIyqir+bjhG+SdkUP1FEMD+TZKA+sEorP8eB8j0oKyo+YI5EP5bsCz7GDxS/3DsvPYEUor8JgJo/hGYPP4jrfL68ln48nzANQAW1HT8zF7o+WP1tv2Q3BT99Kxa/htcbPsJDx78ggNo+RBkKwNONGz/zirC/UAxZvq8tFj/mKBi/wxKWvc3MVj4YZAA+G4aFPBF14T6ABAk++I4Tvy75azyo5LG/X8UaPnp9RD/RkAi97CUyPzNniD7KwT0/vH5YPacLIr9LBzg9aYotv5JKobzRcSQ/IIDaPpFH7T7TjRs/4gyxv+hXLr80aAc/2jgVvuln5r6/Z0092igzPqEzab0G/xA/PjeLPshWBL+J7Fq9MBSwv289rz5F00I/gTw3PS1JUz9cfMa9+JRMPw5cE702Oxa/HB0jPa1KMr/Bfjw80XEkPyCA2j6RR+0+040bP2BmXz9lMp++AoEWPxyCXr+kWT+/ztp8PrKRU7zUc6w+vyNuPXQyR74JIpE+ry/Dv7ZdIL4ntsA9t+BRv3eHDT5sma++AYVPP72ZTr9HBDC/ms5kPe7aBT/Hm3w+0dgOPtFxJD8ggNo+kUftPtONGz+UjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"}, "_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": 31250, "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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 821.2230579375871, "std_reward": 78.6360795335205, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-23T15:03:44.581050"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c5d71fd0c82b37a1ac94a76a91391fb5d5b20d60c4b01e4591a8a737e97e74e
+size 2136