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: 1218.55 +/- 86.26
+      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 sha256:92fcdd206ef982e9220b7be7dc39421b3ed696a5a0a8d07c98c304653e7323c7
+size 128981

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,107 @@
+{
+    "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 0x16dca0670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x16dca0700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x16dca0790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x16dca0820>",
+        "_build": "<function ActorCriticPolicy._build at 0x16dca08b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x16dca0940>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x16dca09d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x16dca0a60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x16dca0af0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x16dca0b80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x16dca0c10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x16dca0ca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x16dca1580>"
+    },
+    "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
+        }
+    },
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1684836364169737000,
+    "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,
+    "_stats_window_size": 100,
+    "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,
+    "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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a83575a803ae6a1ed4dfa341274a6563fb03244b1bf4c7c6361cdbca5f9ac901
+size 55998

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11322e65718d30fcb9ae9d36e08cde0769f954eec0166827864e7066090ad420
+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: macOS-12.1-arm64-arm-64bit Darwin Kernel Version 21.2.0: Sun Nov 28 20:28:41 PST 2021; root:xnu-8019.61.5~1/RELEASE_ARM64_T6000
+- Python: 3.9.16
+- Stable-Baselines3: 1.8.0
+- PyTorch: 1.11.0
+- GPU Enabled: False
+- Numpy: 1.24.3
+- 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 0x16dca0670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x16dca0700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x16dca0790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x16dca0820>", "_build": "<function ActorCriticPolicy._build at 0x16dca08b0>", "forward": "<function ActorCriticPolicy.forward at 0x16dca0940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x16dca09d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x16dca0a60>", "_predict": "<function ActorCriticPolicy._predict at 0x16dca0af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x16dca0b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x16dca0c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x16dca0ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x16dca1580>"}, "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}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1684836364169737000, "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, "_stats_window_size": 100, "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, "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, "system_info": {"OS": "macOS-12.1-arm64-arm-64bit Darwin Kernel Version 21.2.0: Sun Nov 28 20:28:41 PST 2021; root:xnu-8019.61.5~1/RELEASE_ARM64_T6000", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "1.11.0", "GPU Enabled": "False", "Numpy": "1.24.3", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e99f50eb438d2803ccfb23005c8a657cd4e3a1665a153a997915fb04979fca99
+size 1027228

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1218.5520109838048, "std_reward": 86.26422871436363, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-23T12:47:20.062113"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0bd77bbfdf7fba5374bc147435844d344e802fe8faa44fbfc6190ea127ee458b
+size 2252