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: 605.20 +/- 230.87
+      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:f4c5948c1aa1165c3b2a8bb07ce71e42b467a3a01f6efb1100aace672a95986e
+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 0x7f248a938550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f248a9385e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f248a938670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f248a938700>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f248a938790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f248a938820>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f248a9388b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f248a938940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f248a9389d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f248a938a60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f248a938af0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f248a938b80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f248a9345d0>"
+    },
+    "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": 1674295458805150283,
+    "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 sha256:39c4051894552fac5fbda1b880fbed1d5406c55281d1091b57c081d03eb6cce2
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df2980d1550fcda55ac2944354d0a7f703dab7ac88c0e14f2f18debfb78cf5db
+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 0x7f248a938550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f248a9385e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f248a938670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f248a938700>", "_build": "<function ActorCriticPolicy._build at 0x7f248a938790>", "forward": "<function ActorCriticPolicy.forward at 0x7f248a938820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f248a9388b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f248a938940>", "_predict": "<function ActorCriticPolicy._predict at 0x7f248a9389d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f248a938a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f248a938af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f248a938b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f248a9345d0>"}, "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": 1674295458805150283, "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": 605.2026767801872, "std_reward": 230.8661656685603, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-21T11:00:57.842139"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb0a71a851b42849aea9ff13b983cc91e15bcdb89979f0a8e5d791d66d804340
+size 2136