Acrobot-v1 - erster (schlechter) Versuch

Acrobot-v1.zip

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Acrobot-v1/data

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         "__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    ",
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Acrobot-v1/policy.pth

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Acrobot-v1/system_info.txt

@@ -2,7 +2,7 @@
 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.5.1+cu121
-- GPU Enabled: True
+- GPU Enabled: False
 - Numpy: 1.26.4
 - Cloudpickle: 3.1.0
 - Gymnasium: 0.28.1

README.md

@@ -16,7 +16,7 @@ model-index:
       type: Acrobot-v1
     metrics:
     - type: mean_reward
-      value: -500.00 +/- 0.00
+      value: -78.10 +/- 12.89
       name: mean_reward
       verified: false
 ---

config.json

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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 0x78af0c95d630>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78af0c95d6c0>", "reset_noise": 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It allows to keep variance\n        above zero and prevent it from growing too fast. 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results.json

@@ -1 +1 @@
-{"mean_reward": -500.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-11-16T17:03:20.368358"}
+{"mean_reward": -78.1, "std_reward": 12.88759093081403, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-11-16T17:25:33.884731"}