Initial commit of A2C AntBullet

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

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: 1571.56 +/- 109.37
+      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:e8a7b5c8301959b56d90e2bf5477027833fbf255993c24f2f10e8e1576c85c7d
+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 0x7f3f55575950>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3f555759e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3f55575a70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3f55575b00>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3f55575b90>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3f55575c20>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3f55575cb0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3f55575d40>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3f55575dd0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3f55575e60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3f55575ef0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f3f555ce1b0>"
+    },
+    "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": 1668733147258257488,
+    "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": 62505,
+    "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:a0178aeb52371808f372a3ccba07002ba102591f9c4adc8937c9eab3a52e5984
+size 56126

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:afbb7e95f0c18462da9d41a2bc3bc7745dcd2e5678b14f03840a51848bd6ca38
+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 0x7f3f55575950>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3f555759e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3f55575a70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3f55575b00>", "_build": "<function ActorCriticPolicy._build at 0x7f3f55575b90>", "forward": "<function ActorCriticPolicy.forward at 0x7f3f55575c20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3f55575cb0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3f55575d40>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3f55575dd0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3f55575e60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3f55575ef0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f3f555ce1b0>"}, "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": 1668733147258257488, "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": 62505, "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"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ec9b4b14d369360cc960eb532fa08a77c0470e24f8f2ca72c734340a97a5f00
+size 1049098

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1571.5585767919838, "std_reward": 109.36527723371246, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-11-18T02:14:10.909482"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c12273a09ac4e91a20b6ef208438e981e067866d3e3ed183820940a986399d89
+size 2763