Upload PPO Lunar Lander

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 241.22 +/- 43.84
+      value: 242.67 +/- 28.07
       name: mean_reward
       verified: false
 ---

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a11623251d54282966949a84fd4ca3bcd2490625a10c826b4f32da4145ad2d4f
+size 148970

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

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+{
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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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+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f18ab4a5e40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f18ab4a5ee0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f18ab4a5f80>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f18ab4a6020>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f18ab4a60c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f18ab4a6160>",
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+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f18ab4a6480>",
+        "__abstractmethods__": "frozenset()",
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ppo-LunarLander-v2/policy.optimizer.pth

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ppo-LunarLander-v2/pytorch_variables.pth

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ppo-LunarLander-v2/system_info.txt

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+- Python: 3.11.11
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.6.0+cu126
+- GPU Enabled: True
+- Numpy: 2.2.4
+- Cloudpickle: 3.1.1
+- Gymnasium: 0.28.1

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results.json

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