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: 798.37 +/- 155.85
+      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 sha256:546ae4ca78067dd72499124cba100de24f00bc1996414e1c5c2e80be34a7d7a2
+size 129192

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0a2

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 0x7f625bb89160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f625bb891f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f625bb89280>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f625bb89310>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f625bb893a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f625bb89430>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f625bb894c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f625bb89550>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f625bb895e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f625bb89670>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f625bb89700>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f625bb89790>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f625bb7f2d0>"
+    },
+    "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": 1678324606810860300,
+    "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 sha256:14bdf9b2bc7962aafeda0b9f9f277a0a647ac5f16222b98c6a9e38419e8f5f2c
+size 56126

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:791bd95917dcaf05c0fcdb7ca14f6f2487fc61ac1e4dc4f954391dd90aca241b
+size 56894

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-5.15.0-50-generic-x86_64-with-glibc2.29 # 56~20.04.1-Ubuntu SMP Tue Sep 27 15:51:29 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.8.0a2
+- PyTorch: 1.12.0+cu113
+- GPU Enabled: True
+- Numpy: 1.20.1
+- 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 0x7f625bb89160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f625bb891f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f625bb89280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f625bb89310>", "_build": "<function ActorCriticPolicy._build at 0x7f625bb893a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f625bb89430>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f625bb894c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f625bb89550>", "_predict": "<function ActorCriticPolicy._predict at 0x7f625bb895e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f625bb89670>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f625bb89700>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f625bb89790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f625bb7f2d0>"}, "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": 1678324606810860300, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWV2wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMVC9ob21lL2hhbmJrL3RvcmNoX3ZlbnYvbGliL3B5dGhvbjMuOC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMVC9ob21lL2hhbmJrL3RvcmNoX3ZlbnYvbGliL3B5dGhvbjMuOC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/T3UQTVUdaYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_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-5.15.0-50-generic-x86_64-with-glibc2.29 # 56~20.04.1-Ubuntu SMP Tue Sep 27 15:51:29 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.8.0a2", "PyTorch": "1.12.0+cu113", "GPU Enabled": "True", "Numpy": "1.20.1", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70f52a7a061239cc05440ddec830d900098231b65226edcb20f8fd402f7d0543
+size 1112348

results.json

@@ -0,0 +1 @@
+{"mean_reward": 798.3749627737158, "std_reward": 155.8476154678108, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-09T11:45:37.872139"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:30fceac4dff631cbc20f12194f3de4d03f02a3490af480a5db7ce31be556a6fb
+size 2521