model

README.md

@@ -16,16 +16,19 @@ model-index:
       type: Walker2d-v5
     metrics:
     - type: mean_reward
-      value: 6154.44 +/- 787.84
+      value: 6173.86 +/- 197.86
       name: mean_reward
       verified: false
 ---
 
 # **SAC** Agent playing **Walker2d-v5**
+
 This is a trained model of a **SAC** agent playing **Walker2d-v5**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
+
 ## Usage (with Stable-baselines3)
+
 TODO: Add your code
 
 
@@ -34,4 +37,4 @@ from stable_baselines3 import ...
 from huggingface_sb3 import load_from_hub
 
 ...
-```
+```

config.json

@@ -1 +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 0x7fe97d6284a0>", "_build": "<function SACPolicy._build at 0x7fe97d628ae0>", "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7fe97d628b80>", "reset_noise": "<function SACPolicy.reset_noise at 0x7fe97d628c20>", "make_actor": "<function SACPolicy.make_actor at 0x7fe97d628cc0>", "make_critic": "<function SACPolicy.make_critic at 0x7fe97d628d60>", "forward": "<function SACPolicy.forward at 0x7fe97d628e00>", "_predict": "<function SACPolicy._predict at 0x7fe97d628ea0>", "set_training_mode": "<function SACPolicy.set_training_mode at 0x7fe97d628f40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe97d62cf40>"}, "verbose": 0, "policy_kwargs": {"use_sde": false}, "num_timesteps": 20000000, "_total_timesteps": 20000000, "_num_timesteps_at_start": 0, "seed": 0, "action_noise": null, "start_time": 1731416626161730236, "learning_rate": 0.0003, "tensorboard_log": "runs/n58k3j87", "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": 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+        "__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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+        "forward": "<function SACPolicy.forward at 0x7f7385c0d6c0>",
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