third version, tried optuna
Browse files- README.md +1 -1
- config.json +1 -1
- lunar_lander_ppo_v3.zip +3 -0
- lunar_lander_ppo_v3/_stable_baselines3_version +1 -0
- lunar_lander_ppo_v3/data +94 -0
- lunar_lander_ppo_v3/policy.optimizer.pth +3 -0
- lunar_lander_ppo_v3/policy.pth +3 -0
- lunar_lander_ppo_v3/pytorch_variables.pth +3 -0
- lunar_lander_ppo_v3/system_info.txt +7 -0
- replay.mp4 +0 -0
- results.json +1 -1
README.md
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results:
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- metrics:
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- type: mean_reward
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value:
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name: mean_reward
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task:
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type: reinforcement-learning
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results:
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- metrics:
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- type: mean_reward
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value: 271.78 +/- 14.35
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name: mean_reward
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task:
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type: reinforcement-learning
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config.json
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-
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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 0x7f2abbf35710>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2abbf357a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2abbf35830>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2abbf358c0>", "_build": "<function 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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 0x7f4de25914d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4de2591560>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4de25915f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4de2591680>", "_build": "<function 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lunar_lander_ppo_v3/policy.optimizer.pth
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OS: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
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