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: 1218.76 +/- 96.71
+      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:ba02d1c094d1dd5e12f7a171b7853c775f57e3181bae963258cd0e92bc6e63ad
+size 129195

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,105 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f349468ae60>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f349468aef0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f349468af80>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3494691050>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f34946910e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3494691170>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3494691200>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3494691290>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3494691320>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f34946913b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3494691440>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f34946cae70>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gASVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/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": 1667940649684989667,
+    "learning_rate": 0.00096,
+    "tensorboard_log": "./tensorboard",
+    "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:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAAAAACUdJRiLg=="
+    },
+    "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_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 sha256:14bcae13de109e2c7ddfdf660ecf3cc9863fc57f3ec86c16535d03408a233f1f
+size 56126

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0bdc75245166843e6b9265b889fb313d046cb0fe0de47ef681ff403d85f5bae0
+size 56766

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-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.7.15
+Stable-Baselines3: 1.6.2
+PyTorch: 1.12.1+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f349468ae60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f349468aef0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f349468af80>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3494691050>", "_build": "<function ActorCriticPolicy._build at 0x7f34946910e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f3494691170>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3494691200>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3494691290>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3494691320>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f34946913b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3494691440>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f34946cae70>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gASVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/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": 1667940649684989667, "learning_rate": 0.00096, "tensorboard_log": "./tensorboard", "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:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAAAAACUdJRiLg=="}, "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_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-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1218.7621947409934, "std_reward": 96.71347153758067, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-11-08T22:00:48.747410"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:190dbbda1ef46c7115095839a4d610e63bee1271222b07e6d2248d81982f4f64
+size 2763