first dummy solution

LunarLander_Iggg0r_RL_toy_v0.zip

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LunarLander_Iggg0r_RL_toy_v0/_stable_baselines3_version

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+1.7.0

LunarLander_Iggg0r_RL_toy_v0/data

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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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LunarLander_Iggg0r_RL_toy_v0/policy.optimizer.pth

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+size 87545

LunarLander_Iggg0r_RL_toy_v0/policy.pth

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+size 43265

LunarLander_Iggg0r_RL_toy_v0/pytorch_variables.pth

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LunarLander_Iggg0r_RL_toy_v0/system_info.txt

@@ -0,0 +1,7 @@
+- OS: macOS-10.16-x86_64-i386-64bit Darwin Kernel Version 22.2.0: Fri Nov 11 02:03:51 PST 2022; root:xnu-8792.61.2~4/RELEASE_ARM64_T6000
+- Python: 3.8.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1
+- GPU Enabled: False
+- Numpy: 1.24.2
+- Gym: 0.21.0

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 260.98 +/- 25.02
+      value: 281.37 +/- 14.10
       name: mean_reward
       verified: false
 ---

config.json

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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 0x7fde8d8d8af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fde8d8d8b80>", "reset_noise": 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results.json

@@ -1 +1 @@
-{"mean_reward": 260.98297673351794, "std_reward": 25.020275026195673, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-08T23:24:53.285995"}
+{"mean_reward": 281.3745319227213, "std_reward": 14.104102601538244, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-09T00:31:53.200124"}