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8e812ef82f
* CoachInterface + tutorial * Some improvements and typo fixes * merge tutorial 0 and 4 * typo fix + additional tutorial changes * tutorial changes * added reading signals and experiment path argument
85 lines
3.9 KiB
Python
85 lines
3.9 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from typing import Tuple, List
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from rl_coach.base_parameters import AgentParameters, VisualizationParameters, TaskParameters, \
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PresetValidationParameters
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from rl_coach.environments.environment import EnvironmentParameters, Environment
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from rl_coach.filters.filter import NoInputFilter, NoOutputFilter
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from rl_coach.graph_managers.graph_manager import GraphManager, ScheduleParameters
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from rl_coach.level_manager import LevelManager
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from rl_coach.utils import short_dynamic_import
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class BasicRLGraphManager(GraphManager):
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"""
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A basic RL graph manager creates the common scheme of RL where there is a single agent which interacts with a
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single environment.
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"""
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def __init__(self, agent_params: AgentParameters, env_params: EnvironmentParameters,
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schedule_params: ScheduleParameters,
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vis_params: VisualizationParameters=VisualizationParameters(),
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preset_validation_params: PresetValidationParameters = PresetValidationParameters(),
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name='simple_rl_graph'):
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super().__init__(name, schedule_params, vis_params)
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self.agent_params = agent_params
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self.env_params = env_params
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self.preset_validation_params = preset_validation_params
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self.agent_params.visualization = vis_params
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if self.agent_params.input_filter is None:
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if env_params is not None:
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self.agent_params.input_filter = env_params.default_input_filter()
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else:
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# In cases where there is no environment (e.g. batch-rl and imitation learning), there is nowhere to get
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# a default filter from. So using a default no-filter.
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# When there is no environment, the user is expected to define input/output filters (if required) using
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# the preset.
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self.agent_params.input_filter = NoInputFilter()
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if self.agent_params.output_filter is None:
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if env_params is not None:
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self.agent_params.output_filter = env_params.default_output_filter()
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else:
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self.agent_params.output_filter = NoOutputFilter()
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def _create_graph(self, task_parameters: TaskParameters) -> Tuple[List[LevelManager], List[Environment]]:
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# environment loading
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self.env_params.seed = task_parameters.seed
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self.env_params.experiment_path = task_parameters.experiment_path
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env = short_dynamic_import(self.env_params.path)(**self.env_params.__dict__,
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visualization_parameters=self.visualization_parameters)
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# agent loading
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self.agent_params.task_parameters = task_parameters # TODO: this should probably be passed in a different way
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self.agent_params.name = "agent"
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agent = short_dynamic_import(self.agent_params.path)(self.agent_params)
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# set level manager
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level_manager = LevelManager(agents=agent, environment=env, name="main_level")
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return [level_manager], [env]
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def log_signal(self, signal_name, value):
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self.level_managers[0].agents['agent'].agent_logger.create_signal_value(signal_name, value)
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def get_signal_value(self, signal_name):
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return self.level_managers[0].agents['agent'].agent_logger.get_signal_value(signal_name)
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def get_agent(self):
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return self.level_managers[0].agents['agent']
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