model

.gitattributes

@@ -33,3 +33,4 @@ 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:
+- Hopper-v5
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: SAC
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Hopper-v5
+      type: Hopper-v5
+    metrics:
+    - type: mean_reward
+      value: 306.85 +/- 1.08
+      name: mean_reward
+      verified: false
+---
+
+# **SAC** Agent playing **Hopper-v5**
+This is a trained model of a **SAC** agent playing **Hopper-v5**
+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
+
+...
+```

config.json

@@ -0,0 +1 @@
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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    ", "__init__": "<function SACPolicy.__init__ at 0x7f964f004400>", "_build": "<function SACPolicy._build at 0x7f964f004a40>", "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f964f004ae0>", "reset_noise": "<function SACPolicy.reset_noise at 0x7f964f004b80>", "make_actor": "<function SACPolicy.make_actor at 0x7f964f004c20>", "make_critic": "<function SACPolicy.make_critic at 0x7f964f004cc0>", "forward": "<function SACPolicy.forward at 0x7f964f004d60>", "_predict": "<function SACPolicy._predict at 0x7f964f004e00>", "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f964f004ea0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f964eff3d40>"}, "verbose": 0, "policy_kwargs": {"use_sde": false}, "num_timesteps": 50000, "_total_timesteps": 50000, "_num_timesteps_at_start": 0, "seed": 0, "action_noise": null, "start_time": 1729174641785623268, "learning_rate": 0.0003, "tensorboard_log": "runs/7yul7ccz", "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": 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+- Stable-Baselines3: 2.4.0a10
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