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: 1893.58 +/- 194.05
+      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 sha256:1aaddbda217d5ee8c53fd54e10d0ca44fae0d0b0d8714638f9fdcf0ed54d9f7e
+size 129260

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 0x7f4f451f0700>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4f451f0790>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4f451f0820>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4f451f08b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4f451f0940>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4f451f09d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4f451f0a60>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4f451f0af0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4f451f0b80>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4f451f0c10>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4f451f0ca0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4f451f0d30>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f4f451ec5d0>"
+    },
+    "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": 1674470526004328437,
+    "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:b23403fadadaa3a3b935dc25cb661b02e2e339f291d7495539d5fee3f08f72a8
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7def0b1c5d21f62de295b9a8cc01119de0010c04dc9e5dbf0bd291d4b3f1f3a4
+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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- 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 0x7f4f451f0700>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4f451f0790>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4f451f0820>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4f451f08b0>", "_build": "<function ActorCriticPolicy._build at 0x7f4f451f0940>", "forward": "<function ActorCriticPolicy.forward at 0x7f4f451f09d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4f451f0a60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4f451f0af0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4f451f0b80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4f451f0c10>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4f451f0ca0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4f451f0d30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f4f451ec5d0>"}, "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": 1674470526004328437, "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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9b4f419320c031dbdf5aecca597ff6ed49f48732218adb809dbbd9627256a23
+size 1100914

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1893.5771481425625, "std_reward": 194.05156363440668, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-23T12:29:27.785628"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62d07a3e244ee719cad2a6df499365ccb4991fb01afb64e4d74e5fa8f507b96f
+size 2136