Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 291.73 +/- 19.92
+      value: 284.13 +/- 16.48
       name: mean_reward
       verified: false
 ---

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f49ba16d22f9b2254da6c60517ddaab59b9b019dbe85461c8dae41e1e2ebed2d
+size 148171

ppo-LunarLander-v2/_stable_baselines3_version

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

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    ",
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+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

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+oid sha256:d2e77892879c9fd2fd133bf951c37056c18298f4ec13242bab36290a1507b76f
+size 88490

ppo-LunarLander-v2/policy.pth

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+oid sha256:bbf4173350bd6a05fad115f838509f728dade6dd12e92555299c827af95bb538
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

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+size 864

ppo-LunarLander-v2/system_info.txt

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+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

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-{"mean_reward": 291.7345653946682, "std_reward": 19.920243822227153, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-07T02:41:41.043079"}
+{"mean_reward": 284.1297634487495, "std_reward": 16.47880971980381, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-07T02:45:54.348819"}