Message

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -292.00 +/- 130.08
+      value: 260.54 +/- 17.62
       name: mean_reward
       verified: false
 ---

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 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 0x780b2bdec670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x780b2bdec700>", "reset_noise": 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         "__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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-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x780b2bdecb80>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x780b2bdecc10>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x780b2bdecca0>",
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+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c341b8932e0>",
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+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c341b893490>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c341b893520>",
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         "__abstractmethods__": "frozenset()",
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 - OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
-- PyTorch: 2.1.0+cu121
+- PyTorch: 2.2.1+cu121
 - GPU Enabled: True
 - Numpy: 1.25.2
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results.json

@@ -1 +1 @@
-{"mean_reward": -292.0012208219181, "std_reward": 130.07769377244196, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-07T23:35:47.107213"}
+{"mean_reward": 260.5370408, "std_reward": 17.6208645794315, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-14T07:24:57.272399"}