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: 1417.88 +/- 439.89
+      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 sha256:3547138529a056e800545b9ea7b0f76bfc9c3fca23fd84ee993d3e9fed5bf3d0
+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 0x7f0ac6a5edc0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0ac6a5ee50>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0ac6a5eee0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0ac6a5ef70>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f0ac6a62040>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f0ac6a620d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0ac6a62160>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0ac6a621f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f0ac6a62280>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0ac6a62310>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0ac6a623a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0ac6a62430>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f0ac6a58e40>"
+    },
+    "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": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1675022773460906086,
+    "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": 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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:217cd93d58011d9711d81222cd01ff608ac6f04654c16ff9131163f0c6ff69ac
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f704dc318da9381bce6084841cde4d7c736b4389991c3be32e9b6443f3052496
+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: 1.21.6
+- 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 0x7f0ac6a5edc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0ac6a5ee50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0ac6a5eee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0ac6a5ef70>", "_build": "<function ActorCriticPolicy._build at 0x7f0ac6a62040>", "forward": "<function ActorCriticPolicy.forward at 0x7f0ac6a620d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0ac6a62160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0ac6a621f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f0ac6a62280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0ac6a62310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0ac6a623a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0ac6a62430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f0ac6a58e40>"}, "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": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675022773460906086, "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": 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, "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": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1417.8839424158068, "std_reward": 439.8856062862506, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-29T20:59:34.518936"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e825b2c01b47d6e97676a541624c1e3dda68adf428eeffe7ac32a91521d66617
+size 2136