BrainRoster
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Upload PPO LunarLander-v2 trained agent
Browse files- PPO-LunarLander-v2.zip +2 -2
- PPO-LunarLander-v2/data +19 -19
- PPO-LunarLander-v2/policy.optimizer.pth +1 -1
- PPO-LunarLander-v2/policy.pth +1 -1
- README.md +1 -1
- config.json +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
PPO-LunarLander-v2.zip
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PPO-LunarLander-v2/data
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"__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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PPO-LunarLander-v2/policy.pth
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README.md
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type: LunarLander-v2
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metrics:
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---
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type: LunarLander-v2
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metrics:
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value: 279.02 +/- 16.41
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---
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config.json
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replay.mp4
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results.json
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@@ -1 +1 @@
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{"mean_reward":
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{"mean_reward": 279.02207908397463, "std_reward": 16.408865613307004, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-09T12:59:06.279872"}
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