Initial commit

README.md

@@ -1,13 +1,37 @@
-This repo contains a SAC checkpoint trained via [StableBaselines3](https://github.com/DLR-RM/stable-baselines3/blob/c6c660e51bdad1a60e3770e121a88dcb666e7dbf/stable_baselines3/sac/sac.py#L254) on `Ant-v4` from gymnasium with the recommended hyperparameters from [RLZoo](https://github.com/DLR-RM/rl-baselines3-zoo/blob/27e081eb24419ee843ae1c329b0482db823c9fc1/hyperparams/sac.yml#L197):
+---
+library_name: stable-baselines3
+tags:
+- Ant-v4
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: SAC
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Ant-v4
+      type: Ant-v4
+    metrics:
+    - type: mean_reward
+      value: 991.69 +/- 2.55
+      name: mean_reward
+      verified: false
+---
 
-```
-model = SAC("MlpPolicy", env, learning_starts=10000)
-```
+# **SAC** Agent playing **Ant-v4**
+This is a trained model of a **SAC** agent playing **Ant-v4**
+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
 
-The versions are:
+...
 ```
-python==3.12.3
-gymnasium==0.29.1
-stable_baselines3==2.3.2
-torch==2.3.1
-```

SAC-Ant-v4.zip

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SAC-Ant-v4/_stable_baselines3_version

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+2.3.2

SAC-Ant-v4/actor.optimizer.pth

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SAC-Ant-v4/critic.optimizer.pth

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SAC-Ant-v4/data

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+        "__module__": "stable_baselines3.sac.policies",
+        "__annotations__": "{'actor': <class 'stable_baselines3.sac.policies.Actor'>, 'critic': <class 'stable_baselines3.common.policies.ContinuousCritic'>, 'critic_target': <class 'stable_baselines3.common.policies.ContinuousCritic'>}",
+        "__doc__": "\n    Policy class (with both actor and critic) for SAC.\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 use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\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 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    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
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SAC-Ant-v4/pytorch_variables.pth

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SAC-Ant-v4/system_info.txt

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+- OS: macOS-14.4.1-arm64-arm-64bit Darwin Kernel Version 23.4.0: Fri Mar 15 00:12:41 PDT 2024; root:xnu-10063.101.17~1/RELEASE_ARM64_T8103
+- Python: 3.12.3
+- Stable-Baselines3: 2.3.2
+- PyTorch: 2.3.1
+- GPU Enabled: False
+- Numpy: 1.26.4
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.29.1

config.json

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results.json

@@ -0,0 +1 @@
+{"mean_reward": 991.6947078999999, "std_reward": 2.548959651084546, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-10T11:49:51.568057"}