Initial commit

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 1291.10 +/- 55.61
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+---
+
+# **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 sha256:2bed9e014ff446f83c86492f163c3ddb8ee73ea7e165267ea8fb3a44822e7d7d
+size 129194

a2c-AntBulletEnv-v0/_stable_baselines3_version

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

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 0x7fa286ef09e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa286ef0a70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa286ef0b00>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa286ef0b90>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa286ef0c20>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa286ef0cb0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa286ef0d40>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa286ef0dd0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa286ef0e60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa286ef0ef0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa286ef0f80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fa286ecc180>"
+    },
+    "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": 1663512601.0910425,
+    "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 sha256:565634b50889513d95c28f6d78e4e8fc408dea33f52843ad1ca6bcf919ca0092
+size 56126

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d1f83bcc9de9bee532e6040af848c499fa7e196107d3c99ac2e0c7d4b008f56
+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: 3.7.14
+Stable-Baselines3: 1.6.0
+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 0x7fa286ef09e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa286ef0a70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa286ef0b00>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa286ef0b90>", "_build": "<function ActorCriticPolicy._build at 0x7fa286ef0c20>", "forward": "<function ActorCriticPolicy.forward at 0x7fa286ef0cb0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa286ef0d40>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa286ef0dd0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa286ef0e60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa286ef0ef0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa286ef0f80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa286ecc180>"}, "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": 1663512601.0910425, "learning_rate": 0.00096, "tensorboard_log": "./tensorboard", "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gASVwQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP091EE1VHWmFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "_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": "3.7.14", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1291.1035922251874, "std_reward": 55.61307883671822, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-09-18T15:47:14.462584"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebb518449c760c2a398ce9e90ac9326530475c52bd0d27df32e73dfc5e606c24
+size 2763