Initial commit

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- HalfCheetahBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - metrics:
+    - type: mean_reward
+      value: -755.96 +/- 225.20
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: HalfCheetahBulletEnv-v0
+      type: HalfCheetahBulletEnv-v0
+---
+
+# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-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-HalfCheetahBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f3336148f377c8a24638547595e269e071a68cec3a244343ff51785156ba524
+size 123690

a2c-HalfCheetahBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.0

a2c-HalfCheetahBulletEnv-v0/data

@@ -0,0 +1,102 @@
+{
+    "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 0x7f221d86f680>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f221d86f710>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f221d86f7a0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f221d86f830>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f221d86f8c0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f221d86f950>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f221d86f9e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f221d86fa70>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f221d86fb00>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f221d86fb90>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f221d86fc20>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f221d8b0d50>"
+    },
+    "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": [
+            26
+        ],
+        "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]",
+        "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]",
+        "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]",
+        "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]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            6
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 1,
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1659810005.689662,
+    "learning_rate": 0.00096,
+    "tensorboard_log": "./tensorboard",
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gASV8gAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwFLGoaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiUNoAAAAAAAAAAAAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIC3A5G+AAAAAEEoj70AAAAA9G5svgAAAACa028+AAAAALNmKr0AAAAAYeSjPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gASViQAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwGFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDAQGUdJRiLg=="
+    },
+    "_last_original_obs": null,
+    "_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": 250000,
+    "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-HalfCheetahBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f27cf43879fbfa16ba647a53d85d9a0e17508a42cebf6551fcfdf51693efed4
+size 54142

a2c-HalfCheetahBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:045e3b4a5209a8d309d9921f34f134c82f46b9a1b07dc54f2ac5e7a73d6c932a
+size 54718

a2c-HalfCheetahBulletEnv-v0/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-HalfCheetahBulletEnv-v0/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.6.0
+PyTorch: 1.12.0+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 0x7f221d86f680>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f221d86f710>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f221d86f7a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f221d86f830>", "_build": "<function ActorCriticPolicy._build at 0x7f221d86f8c0>", "forward": "<function ActorCriticPolicy.forward at 0x7f221d86f950>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f221d86f9e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f221d86fa70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f221d86fb00>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f221d86fb90>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f221d86fc20>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f221d8b0d50>"}, "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": [26], "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]", "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]", "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]", "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]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [6], "low": "[-1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True]", "_np_random": null}, "n_envs": 1, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1659810005.689662, "learning_rate": 0.00096, "tensorboard_log": "./tensorboard", "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASV8gAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwFLGoaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiUNoAAAAAAAAAAAAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIC3A5G+AAAAAEEoj70AAAAA9G5svgAAAACa028+AAAAALNmKr0AAAAAYeSjPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASViQAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwGFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDAQGUdJRiLg=="}, "_last_original_obs": null, "_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": 250000, "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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -755.9607626668833, "std_reward": 225.20464390631574, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-08-06T20:10:55.870962"}