wehub-resource-sync
121 lines
3.9 KiB
Python
121 lines
3.9 KiB
Python
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
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from ray.rllib.algorithms.marwil.marwil import MARWIL, MARWILConfig
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from ray.rllib.core.rl_module.rl_module import RLModuleSpec
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.typing import RLModuleSpecType
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class BCConfig(MARWILConfig):
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"""Defines a configuration class from which a new BC Algorithm can be built
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.. testcode::
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:skipif: True
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from ray.rllib.algorithms.bc import BCConfig
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# Run this from the ray directory root.
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config = BCConfig().training(lr=0.00001, gamma=0.99)
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config = config.offline_data(
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input_="./rllib/offline/tests/data/cartpole/large.json")
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# Build an Algorithm object from the config and run 1 training iteration.
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algo = config.build()
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algo.train()
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.. testcode::
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:skipif: True
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from ray.rllib.algorithms.bc import BCConfig
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from ray import tune
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config = BCConfig()
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# Print out some default values.
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print(config.beta)
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# Update the config object.
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config.training(
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lr=tune.grid_search([0.001, 0.0001]), beta=0.75
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)
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# Set the config object's data path.
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# Run this from the ray directory root.
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config.offline_data(
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input_="./rllib/offline/tests/data/cartpole/large.json"
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)
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# Set the config object's env, used for evaluation.
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config.environment(env="CartPole-v1")
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# Use to_dict() to get the old-style python config dict
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# when running with tune.
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tune.Tuner(
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"BC",
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param_space=config.to_dict(),
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).fit()
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"""
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def __init__(self, algo_class=None):
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super().__init__(algo_class=algo_class or BC)
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# fmt: off
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# __sphinx_doc_begin__
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# No need to calculate advantages (or do anything else with the rewards).
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self.beta = 0.0
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# Advantages (calculated during postprocessing)
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# not important for behavioral cloning.
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self.postprocess_inputs = False
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# Materialize only the mapped data. This is optimal as long
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# as no connector in the connector pipeline holds a state.
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self.materialize_data = False
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self.materialize_mapped_data = True
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# __sphinx_doc_end__
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# fmt: on
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@override(AlgorithmConfig)
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def get_default_rl_module_spec(self) -> RLModuleSpecType:
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if self.framework_str == "torch":
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from ray.rllib.algorithms.bc.torch.default_bc_torch_rl_module import (
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DefaultBCTorchRLModule,
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)
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return RLModuleSpec(module_class=DefaultBCTorchRLModule)
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else:
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raise ValueError(
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f"The framework {self.framework_str} is not supported. "
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"Use `torch` instead."
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)
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@override(AlgorithmConfig)
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def build_learner_connector(
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self,
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input_observation_space,
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input_action_space,
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device=None,
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):
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pipeline = super().build_learner_connector(
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input_observation_space=input_observation_space,
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input_action_space=input_action_space,
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device=device,
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)
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# Remove unneeded connectors from the MARWIL connector pipeline.
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pipeline.remove("AddOneTsToEpisodesAndTruncate")
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pipeline.remove("GeneralAdvantageEstimation")
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return pipeline
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@override(MARWILConfig)
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def validate(self) -> None:
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# Call super's validation method.
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super().validate()
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if self.beta != 0.0:
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self._value_error("For behavioral cloning, `beta` parameter must be 0.0!")
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class BC(MARWIL):
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"""Behavioral Cloning (derived from MARWIL).
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Uses MARWIL with beta force-set to 0.0.
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"""
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@classmethod
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@override(MARWIL)
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def get_default_config(cls) -> BCConfig:
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return BCConfig()
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