Initial commit

.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: 1524.27 +/- 157.24
+      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 sha256:7e3c1561259b254a11abf84656ce105291ba7ac26fa4289ddf98f9e801f627bd
+size 129265

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 0x7f000b4d9700>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f000b4d9790>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f000b4d9820>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f000b4d98b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f000b4d9940>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f000b4d99d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f000b4d9a60>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f000b4d9af0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f000b4d9b80>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f000b4d9c10>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f000b4d9ca0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f000b4d9d30>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f000b4da780>"
+    },
+    "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": 1679214383483375852,
+    "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:ea8b7bb90d8ae3ac9d88dc58117b7dd70340a67a6da8d98a208b5388ba14e0aa
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:845d926fe36c187aa459402038857aed83ebde9bed5173ccce12488e2531d641
+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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- 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 0x7f000b4d9700>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f000b4d9790>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f000b4d9820>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f000b4d98b0>", "_build": "<function ActorCriticPolicy._build at 0x7f000b4d9940>", "forward": "<function ActorCriticPolicy.forward at 0x7f000b4d99d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f000b4d9a60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f000b4d9af0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f000b4d9b80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f000b4d9c10>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f000b4d9ca0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f000b4d9d30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f000b4da780>"}, "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:": "gAWVZwIAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLHIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWcAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/lGgKSxyFlIwBQ5R0lFKUjARoaWdolGgSKJZwAAAAAAAAAAAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH+UaApLHIWUaBV0lFKUjA1ib3VuZGVkX2JlbG93lGgSKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLHIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaCFLHIWUaBV0lFKUjApfbnBfcmFuZG9tlE51Yi4=", "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": 1679214383483375852, "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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c781b56b1432cafa25be692c78a7afc1a00f6d63c47fe830b5c59045d0ae842d
+size 1277475

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1524.2693508512457, "std_reward": 157.2443136765687, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-19T09:31:55.991689"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3982ee0637f60871b43ecc01032f18db0f45f3be285bc9deb8bd36aac17e7203
+size 2136