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: 1102.58 +/- 114.93
+      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 sha256:235917aa48ee18c32c313c5678561a101575ae71773adf277c5327fdbc8e6aab
+size 129256

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 0x7f54bb534af0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f54bb534b80>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f54bb534c10>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f54bb534ca0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f54bb534d30>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f54bb534dc0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f54bb534e50>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f54bb534ee0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f54bb534f70>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f54bb538040>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f54bb5380d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f54bb538160>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f54bb52dd20>"
+    },
+    "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": 1674228930520306299,
+    "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 sha256:4cd3cc71db8b2d38c605fab2f7f27855e444233ac048805c813d5c2df0f63542
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee602a262f774c88aa63601a68dfdc370032a448a9ce21873706cfb0cdc67001
+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 0x7f54bb534af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f54bb534b80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f54bb534c10>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f54bb534ca0>", "_build": "<function ActorCriticPolicy._build at 0x7f54bb534d30>", "forward": "<function ActorCriticPolicy.forward at 0x7f54bb534dc0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f54bb534e50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f54bb534ee0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f54bb534f70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f54bb538040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f54bb5380d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f54bb538160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f54bb52dd20>"}, "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": 1674228930520306299, "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": 1102.5756510415727, "std_reward": 114.93257805065372, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-20T16:30:30.442421"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dbaacfb6afb9712d9f70e95061a0da69988bd0aaccf6907998521906d7c20eea
+size 2136