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: 1605.87 +/- 88.93
+      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:ca2342d2b57273f9c3d9a77f84c5faac90557c1605365b97175980bffcd3be47
+size 129360

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 0x7ff44dc85670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff44dc85700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff44dc85790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff44dc85820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff44dc858b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff44dc85940>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff44dc859d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff44dc85a60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff44dc85af0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff44dc85b80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff44dc85c10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff44dc85ca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ff44dc87900>"
+    },
+    "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": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676434621740895506,
+    "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:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAAAAAAAc6YM1AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAA0CIvQAAAAB+Zve/AAAAAKSQ+r0AAAAAEwTePwAAAADy4LQ9AAAAAJsr5z8AAAAA2WOJuwAAAADFi++/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA+RBeNQAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgDatzr0AAAAA2Bb/vwAAAAAIxac9AAAAAD3z7D8AAAAA5KXivAAAAABhjO8/AAAAAHFt+D0AAAAATQ3zvwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI2MobUAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIDDS/w9AAAAAG5D578AAAAAehEEvgAAAAC7wv8/AAAAACA+Az4AAAAAVp/gPwAAAAAm/I29AAAAALQu2r8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABiSQYyAACAPwAAAAAAAAAAAAAAAAAAAAAAAACAc/7aPQAAAAD+3+2/AAAAAF1L4D0AAAAAetD3PwAAAADqgAq9AAAAALtK2z8AAAAAdHrTvAAAAAC6suG/AAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"
+    },
+    "_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": 31250,
+    "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 sha256:169f4480f7a7a52c1f753654d78188e2be93e224d7a99bc3ffd7348a6b286d62
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd2b1b8e3f38c30d8e6b6becbce5cbc4d30ce4b4bf785fb521255877ada2040c
+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-5.15.0-60-generic-x86_64-with-glibc2.35 # 66-Ubuntu SMP Fri Jan 20 14:29:49 UTC 2023
+- Python: 3.9.0
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: True
+- Numpy: 1.24.2
+- 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 0x7ff44dc85670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff44dc85700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff44dc85790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff44dc85820>", "_build": "<function ActorCriticPolicy._build at 0x7ff44dc858b0>", "forward": "<function ActorCriticPolicy.forward at 0x7ff44dc85940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff44dc859d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff44dc85a60>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff44dc85af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff44dc85b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff44dc85c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff44dc85ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff44dc87900>"}, "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": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676434621740895506, "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": 31250, "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-5.15.0-60-generic-x86_64-with-glibc2.35 # 66-Ubuntu SMP Fri Jan 20 14:29:49 UTC 2023", "Python": "3.9.0", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.24.2", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5257d33d02ae0edf5a2fcb8cc3cbd0403abd84126da2e0578ac349b24ad7ee7
+size 1088742

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1605.8696815082803, "std_reward": 88.92677966558351, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-14T22:01:15.752580"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f24824c7c9aa7709fba6f1a3dfa454e32c74df97c5482749d545d07d501731f
+size 2218