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: 1423.65 +/- 98.98
+      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 sha256:085bc671f925410b0f74bd93c4234fc4ab48b6a2ee7013f64391ef41df9cd1fc
+size 129299

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 0x7fee9d381240>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fee9d3812d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fee9d381360>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fee9d3813f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fee9d381480>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fee9d381510>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fee9d3815a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fee9d381630>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fee9d3816c0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fee9d381750>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fee9d3817e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fee9d381870>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fee9d373f80>"
+    },
+    "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": 1683319439674983343,
+    "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:": "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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 sha256:26bc51bbf652fb733c8a8ce46e29fca640a73eb49b5fb970b77310cb1f51d985
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b79e661d2c5ba51fb2a795a12785769ee9709ee2be22871ffe5cf835b8f0ff21
+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-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 # 1 SMP Fri Jan 27 02:56:13 UTC 2023
+- Python: 3.10.9
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.0+cu117
+- GPU Enabled: True
+- 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 0x7fee9d381240>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fee9d3812d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fee9d381360>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fee9d3813f0>", "_build": "<function ActorCriticPolicy._build at 0x7fee9d381480>", "forward": "<function ActorCriticPolicy.forward at 0x7fee9d381510>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fee9d3815a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fee9d381630>", "_predict": "<function ActorCriticPolicy._predict at 0x7fee9d3816c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fee9d381750>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fee9d3817e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fee9d381870>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fee9d373f80>"}, "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": 1683319439674983343, "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:": "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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, "system_info": {"OS": "Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 # 1 SMP Fri Jan 27 02:56:13 UTC 2023", "Python": "3.10.9", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu117", "GPU Enabled": "True", "Numpy": "1.24.3", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18d3013f501f3049866b0d52e84551d08c92f87359d740eec76aa1c32ec9a4d1
+size 1088307

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1423.648964334937, "std_reward": 98.97506721865545, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-05T18:31:36.400591"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:207ce9a29931036faecd1b94a4b6f9b7916c4dff898ef2d07bf5c2afc3536e70
+size 2493