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: 1402.59 +/- 168.02
+      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 sha256:543383ea96f52208584de1d8a819cee2d9821c093572ac43bb6d80b5b007a2cf
+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 0x7fa84375ee50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa84375eee0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa84375ef70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa843764040>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa8437640d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa843764160>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa8437641f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa843764280>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa843764310>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa8437643a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa843764430>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa8437644c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fa843761e80>"
+    },
+    "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:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAIC/AACAvwAAgL8AAIC/AACAvwAAgL8AAIC/AACAv5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIA/AACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAP5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAQEBAQEBAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAEBAQEBAQEBlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "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": 1679961484222761262,
+    "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 sha256:27fec19e7d2c55f1958765e9e5316853569b386d3339115a2a17f7fb5c423bfd
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9ab77e43b65769f3a53e0402841689425a711bb07e615bb35f85e664bbfc2a9
+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 0x7fa84375ee50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa84375eee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa84375ef70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa843764040>", "_build": "<function ActorCriticPolicy._build at 0x7fa8437640d0>", "forward": "<function ActorCriticPolicy.forward at 0x7fa843764160>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa8437641f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa843764280>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa843764310>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa8437643a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa843764430>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa8437644c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa843761e80>"}, "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": 1679961484222761262, "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:b360eef9952426ef4c10d1b6076a48149af0386109fd2e3edac6b879c072a416
+size 1123284

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1402.5942258923837, "std_reward": 168.02459260883822, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-28T01:03:11.924649"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9d175e0969387a5d53b30ca702914e20c569cdafe326ba0b822902f3428037c
+size 2136