Adding Lunar Lander Trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 262.96 +/- 18.65
+      value: 256.74 +/- 20.03
       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 0x7accd6eafc70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7accd6eafd00>", "reset_noise": 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It allows to keep variance\n        above zero and prevent it from growing too fast. 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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    ",
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+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7cdcf61edab0>",
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ppo-LunarLander-v2/policy.optimizer.pth

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-size 87978
+oid sha256:71f89031e7f96d8a4f68b4d58fb4c315b23a697773276d4b0bdf4b27a3d3f228
+size 88362

ppo-LunarLander-v2/policy.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:f04e3d5bc98ebc6971a70f6c99b3b3f547f185c8b93378a6d5cdcba73f0ae5c1
-size 43634
+oid sha256:c0a7fa83e77160ab4dc1cc12cd4ddd9943ae1918ce4cddb9f8c341051898c6b1
+size 43762

ppo-LunarLander-v2/system_info.txt

@@ -1,8 +1,8 @@
-- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- 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+cu118
-- GPU Enabled: False
+- PyTorch: 2.1.0+cu121
+- GPU Enabled: True
 - Numpy: 1.23.5
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1

results.json

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-{"mean_reward": 262.9618266, "std_reward": 18.651914795630432, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-07T19:32:11.716130"}
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