reagent.model_managers.parametric package

Submodules

reagent.model_managers.parametric.parametric_dqn module

class reagent.model_managers.parametric.parametric_dqn.ParametricDQN(state_preprocessing_options: Optional[reagent.workflow.types.PreprocessingOptions] = None, action_preprocessing_options: Optional[reagent.workflow.types.PreprocessingOptions] = None, state_float_features: Optional[List[Tuple[int, str]]] = None, action_float_features: Optional[List[Tuple[int, str]]] = None, reader_options: Optional[reagent.workflow.types.ReaderOptions] = None, eval_parameters: reagent.core.parameters.EvaluationParameters = <factory>, trainer_param: reagent.training.parameters.ParametricDQNTrainerParameters = <factory>, net_builder: reagent.net_builder.unions.ParametricDQNNetBuilder__Union = <factory>)

Bases: reagent.model_managers.parametric_dqn_base.ParametricDQNBase

build_serving_module(trainer_module: reagent.training.reagent_lightning_module.ReAgentLightningModule, normalization_data_map: Dict[str, reagent.core.parameters.NormalizationData]) torch.nn.modules.module.Module

Optionaly, implement this method if you only have one model for serving

build_trainer(normalization_data_map: Dict[str, reagent.core.parameters.NormalizationData], use_gpu: bool, reward_options: Optional[reagent.workflow.types.RewardOptions] = None) reagent.training.parametric_dqn_trainer.ParametricDQNTrainer

Implement this to build the trainer, given the config

TODO: This function should return ReAgentLightningModule & the dictionary of modules created

net_builder: reagent.net_builder.unions.ParametricDQNNetBuilder__Union
property rl_parameters
trainer_param: reagent.training.parameters.ParametricDQNTrainerParameters

Module contents

class reagent.model_managers.parametric.ParametricDQN(state_preprocessing_options: Optional[reagent.workflow.types.PreprocessingOptions] = None, action_preprocessing_options: Optional[reagent.workflow.types.PreprocessingOptions] = None, state_float_features: Optional[List[Tuple[int, str]]] = None, action_float_features: Optional[List[Tuple[int, str]]] = None, reader_options: Optional[reagent.workflow.types.ReaderOptions] = None, eval_parameters: reagent.core.parameters.EvaluationParameters = <factory>, trainer_param: reagent.training.parameters.ParametricDQNTrainerParameters = <factory>, net_builder: reagent.net_builder.unions.ParametricDQNNetBuilder__Union = <factory>)

Bases: reagent.model_managers.parametric_dqn_base.ParametricDQNBase

build_serving_module(trainer_module: reagent.training.reagent_lightning_module.ReAgentLightningModule, normalization_data_map: Dict[str, reagent.core.parameters.NormalizationData]) torch.nn.modules.module.Module

Optionaly, implement this method if you only have one model for serving

build_trainer(normalization_data_map: Dict[str, reagent.core.parameters.NormalizationData], use_gpu: bool, reward_options: Optional[reagent.workflow.types.RewardOptions] = None) reagent.training.parametric_dqn_trainer.ParametricDQNTrainer

Implement this to build the trainer, given the config

TODO: This function should return ReAgentLightningModule & the dictionary of modules created

eval_parameters: EvaluationParameters
net_builder: reagent.net_builder.unions.ParametricDQNNetBuilder__Union
property rl_parameters
trainer_param: reagent.training.parameters.ParametricDQNTrainerParameters