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: 1073.77 +/- 101.15
+      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 sha256:4983a19ecdafd18bcca6f81f16900663d770e18121e9aaf5baf10085cd2bd6f5
+size 129248

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 0x7f5565cd9ea0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5565cd9f30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5565cd9fc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5565cda050>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5565cda0e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5565cda170>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5565cda200>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5565cda290>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5565cda320>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5565cda3b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5565cda440>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5565cda4d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f5565cd5cc0>"
+    },
+    "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": 1686049826259399798,
+    "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:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAAAAAABgvHc2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAR8mrvQAAAACqB/S/AAAAANrbrz0AAAAAWwYAQAAAAAAg/aA7AAAAAEbF6j8AAAAADKKxPAAAAADRT+G/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAASM1etgAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgFNjg70AAAAAaB/cvwAAAADHC4m9AAAAACP12T8AAAAAy5/kvAAAAABJCd4/AAAAABBZ5j0AAAAA8a/ovwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAERWA7cAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIBhCJc9AAAAAJWW4L8AAAAARfUJvgAAAAAo9vw/AAAAAIjLlr0AAAAAbz/wPwAAAABlF3g9AAAAAA9P3r8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACSinU2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACA2uIQvgAAAABDvPq/AAAAAO8sAjwAAAAACK7hPwAAAABnEgY+AAAAAC5u6z8AAAAA0rb1PQAAAADaHua/AAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"
+    },
+    "_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 sha256:1de621615222ac863ab6babe233745bb45707eadec36a622e37a0dd2d7f5e789
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23d078e13edfe8651f9e390b46ea7c04b748d5ef1ef16f1d62252c25978c0d4d
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.11
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+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 0x7f5565cd9ea0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5565cd9f30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5565cd9fc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5565cda050>", "_build": "<function ActorCriticPolicy._build at 0x7f5565cda0e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f5565cda170>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5565cda200>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5565cda290>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5565cda320>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5565cda3b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5565cda440>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5565cda4d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f5565cd5cc0>"}, "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": 1686049826259399798, "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, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1073.7725581053498, "std_reward": 101.14829995354818, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-06T12:12:07.759964"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5393c24aa159654ca269067b9367d7da1131a84bee2df5f88dc14a0d69eeda90
+size 2176