feat: PPO agent trained on LunaLander-v2
Browse files- README.md +6 -5
- config.json +1 -1
- ppo_LunaLander_v2.zip +3 -0
- ppo_LunaLander_v2/_stable_baselines3_version +1 -0
- ppo_LunaLander_v2/data +95 -0
- ppo_LunaLander_v2/policy.optimizer.pth +3 -0
- ppo_LunaLander_v2/policy.pth +3 -0
- ppo_LunaLander_v2/pytorch_variables.pth +3 -0
- ppo_LunaLander_v2/system_info.txt +7 -0
- replay.mp4 +2 -2
- results.json +1 -1
README.md
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model-index:
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- name: PPO
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results:
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- type: mean_reward
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value: 175.86 +/- 106.43
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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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# **PPO** Agent playing **LunarLander-v2**
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model-index:
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- name: PPO
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results:
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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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metrics:
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- type: mean_reward
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value: 243.95 +/- 10.34
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name: mean_reward
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verified: false
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---
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# **PPO** Agent playing **LunarLander-v2**
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config.json
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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 0x7fc62674da70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc62674db00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc62674db90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc62674dc20>", "_build": "<function 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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 0x7fabd0a5d820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fabd0a5d8b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fabd0a5d940>", 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ppo_LunaLander_v2/policy.optimizer.pth
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ppo_LunaLander_v2/policy.pth
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ppo_LunaLander_v2/pytorch_variables.pth
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ppo_LunaLander_v2/system_info.txt
ADDED
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|
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|
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|
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|
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|
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replay.mp4
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results.json
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{"mean_reward":
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