tobyych commited on
Commit
abc850e
1 Parent(s): a890b45

upload PPO LunarLander-v2 trained agent

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.gitattributes CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - LunarLander-v2
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - stable-baselines3
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+ model-index:
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+ - name: PPO
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+ results:
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+ - metrics:
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+ - type: mean_reward
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+ value: -16.50 +/- 57.79
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+ name: mean_reward
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+ task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: LunarLander-v2
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+ type: LunarLander-v2
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+ ---
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+
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+ # **PPO** Agent playing **LunarLander-v2**
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+ This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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+
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+ ## Usage (with Stable-baselines3)
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+ TODO: Add your code
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+
config.json ADDED
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If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7fa5e0ac75e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa5e0ac7670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa5e0ac7700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa5e0ac7790>", "_build": "<function 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results.json ADDED
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+ {"mean_reward": -16.497897369543352, "std_reward": 57.79133704892941, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-13T22:31:16.189889"}