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: 1471.27 +/- 352.17
+      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:b1e3b73c4e03bf6476ba5eb5763cc0258dca55c7ca1a1a4685b4db961687ff0d
+size 129005

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 0x7f92f07ae820>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f92f07ae8b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f92f07ae940>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f92f07ae9d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f92f07aea60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f92f07aeaf0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f92f07aeb80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f92f07aec10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f92f07aeca0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f92f07aed30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f92f07aedc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f92f07aee50>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f92f07a5ed0>"
+    },
+    "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": 1674321453874224608,
+    "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:f0ec21daafaa28915cc516e6700fb9329ab7723cfdd2f90bb95047de75ac81ea
+size 56062

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c3ddaa7ac1a7a1564ee32c487c800430654878e316aaacf42ff181422755e33
+size 56830

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: False
+- 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 0x7f92f07ae820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f92f07ae8b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f92f07ae940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f92f07ae9d0>", "_build": "<function ActorCriticPolicy._build at 0x7f92f07aea60>", "forward": "<function ActorCriticPolicy.forward at 0x7f92f07aeaf0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f92f07aeb80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f92f07aec10>", "_predict": "<function ActorCriticPolicy._predict at 0x7f92f07aeca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f92f07aed30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f92f07aedc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f92f07aee50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f92f07a5ed0>"}, "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": 1674321453874224608, "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": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67a482f44c44cfd2d149d82554a2c454f756db312faf3887ffd85d9d43cd3bae
+size 1067452

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1471.2680601364948, "std_reward": 352.17457175406616, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-21T18:28:02.814721"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbfdd3db0e0d6d0e60c1e0e670d3dd8f2929bf1791767d71c00154c1b80e3f48
+size 2136