Upload PPO trained on LunarLander-v2

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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PPO-LunarLander-v2/policy.optimizer.pth

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-oid sha256:81d3f1859b56d2905bb2ab0f4c11d55f624f5ff3df0a1d0235f548c3a7c8d8c3
+oid sha256:342ba02b6ea80313418bf0d20a1bc6a0488aa827e5a95557e8239db8cee627ab
 size 87978

PPO-LunarLander-v2/policy.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:dc0c2fcf9dc2f3cbdd7a9ac862ef4550a9650d6019bdca6de70a961722e75477
+oid sha256:9252d4e02b6a5c79a22d7877811cb27a0f525aaac2df7ec85191f68e0b8ea9b1
 size 43634

README.md

@@ -6,7 +6,7 @@ tags:
 - reinforcement-learning
 - stable-baselines3
 model-index:
-- name: ppo-LunarLander-v2
+- name: PPO
   results:
   - task:
       type: reinforcement-learning
@@ -16,13 +16,13 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -198.68 +/- 21.22
+      value: 251.53 +/- 33.99
       name: mean_reward
       verified: false
 ---
 
-# **ppo-LunarLander-v2** Agent playing **LunarLander-v2**
-This is a trained model of a **ppo-LunarLander-v2** agent playing **LunarLander-v2**
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
 ## Usage (with Stable-baselines3)

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x780aed7dfba0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x780aed7dfc40>", "reset_noise": 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replay.mp4

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 version https://git-lfs.github.com/spec/v1
-oid sha256:f0c0cc6fe85b172679a9b8b7e1a98bdd1189e3fd8d357b08bfb5e4ec89ebe9e3
-size 185694
+oid sha256:d2b9e87eaf308c085215832d44b7fec9c3af576dab7606e26b40fc3bb15ff376
+size 166894

results.json

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-{"mean_reward": -198.68286882638932, "std_reward": 21.221517051959534, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-06-24T10:35:17.461675"}
+{"mean_reward": 251.5253254, "std_reward": 33.991387921417015, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-06-26T13:52:20.945083"}