Better landing!
Browse files- README.md +1 -1
- Yazan_moon_lander_V2.zip +3 -0
- Yazan_moon_lander_V2/_stable_baselines3_version +1 -0
- Yazan_moon_lander_V2/data +95 -0
- Yazan_moon_lander_V2/policy.optimizer.pth +3 -0
- Yazan_moon_lander_V2/policy.pth +3 -0
- Yazan_moon_lander_V2/pytorch_variables.pth +3 -0
- Yazan_moon_lander_V2/system_info.txt +7 -0
- config.json +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
README.md
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@@ -16,7 +16,7 @@ model-index:
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type: LunarLander-v2
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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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verified: false
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---
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value: 295.92 +/- 19.30
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name: mean_reward
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verified: false
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---
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Yazan_moon_lander_V2.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:f1b7e7e996700cf3d1addaada1d12bc70ff7133d0cbea8e54b2f0dad1f692cb8
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size 147422
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Yazan_moon_lander_V2/_stable_baselines3_version
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1.7.0
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Yazan_moon_lander_V2/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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"__module__": "stable_baselines3.common.policies",
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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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"__init__": "<function ActorCriticPolicy.__init__ at 0x7fa9f2a46670>",
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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 0x7fa9f2a46670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa9f2a46700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa9f2a46790>", 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