# # Copyright (c) 2017 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from typing import Tuple, List from rl_coach.base_parameters import AgentParameters, VisualizationParameters, TaskParameters, PresetValidationParameters from rl_coach.environments.environment import EnvironmentParameters, Environment from rl_coach.level_manager import LevelManager from rl_coach.graph_managers.graph_manager import GraphManager, ScheduleParameters from rl_coach.utils import short_dynamic_import class BasicRLGraphManager(GraphManager): """ A basic RL graph manager creates the common scheme of RL where there is a single agent which interacts with a single environment. """ def __init__(self, agent_params: AgentParameters, env_params: EnvironmentParameters, schedule_params: ScheduleParameters, vis_params: VisualizationParameters=VisualizationParameters(), preset_validation_params: PresetValidationParameters = PresetValidationParameters()): super().__init__('simple_rl_graph', schedule_params, vis_params) self.agent_params = agent_params self.env_params = env_params self.preset_validation_params = preset_validation_params self.agent_params.visualization = vis_params if self.agent_params.input_filter is None: self.agent_params.input_filter = env_params.default_input_filter() if self.agent_params.output_filter is None: self.agent_params.output_filter = env_params.default_output_filter() def _create_graph(self, task_parameters: TaskParameters) -> Tuple[List[LevelManager], List[Environment]]: # environment loading self.env_params.seed = task_parameters.seed env = short_dynamic_import(self.env_params.path)(**self.env_params.__dict__, visualization_parameters=self.visualization_parameters) # agent loading self.agent_params.task_parameters = task_parameters # TODO: this should probably be passed in a different way self.agent_params.name = "agent" agent = short_dynamic_import(self.agent_params.path)(self.agent_params) # set level manager level_manager = LevelManager(agents=agent, environment=env, name="main_level") return [level_manager], [env]