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README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 223.35 +/- 80.59
+      value: 279.00 +/- 15.83
       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 0x7da8e4a2caf0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7da8e4a2cb80>", "reset_noise": 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ppo-LunarLander-v2.zip

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ppo-LunarLander-v2/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    ",
-        "__init__": "<function ActorCriticPolicy.__init__ at 0x7da8e4a2caf0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7da8e4a2cb80>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7da8e4a2cc10>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7da8e4a2cca0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7da8e4a2cd30>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7da8e4a2cdc0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7da8e4a2ce50>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7da8e4a2cee0>",
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ppo-LunarLander-v2/policy.optimizer.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:e80d46bf9b69ff12f459b1f2e61c4841eba5e735cf9c9b59e41f28ef055ab3f6
-size 88362
+oid sha256:d252b2ad2ba316d6cd6b3ee22e29aa55036d82d754d3cf10ab78da9e750491e1
+size 87978

ppo-LunarLander-v2/policy.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:87062ca59d0cda3f06cab51ba4be75560185e1c2d4ee736046da7d41027629c2
-size 43762
+oid sha256:346e8a61edfceed4e48bfc1fe68431a15cddd0dfe1a448984e332704de9c124a
+size 43634

ppo-LunarLander-v2/system_info.txt

@@ -2,7 +2,7 @@
 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.1.0+cu121
-- GPU Enabled: True
+- GPU Enabled: False
 - Numpy: 1.25.2
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1

results.json

@@ -1 +1 @@
-{"mean_reward": 223.34998009999998, "std_reward": 80.58648939532951, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-05T14:14:54.612048"}
+{"mean_reward": 279.00388269999996, "std_reward": 15.825199841561911, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-06T09:23:31.386105"}