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84 行
3.3 KiB
84 行
3.3 KiB
from typing import Dict, List
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from mlagents_envs.base_env import BaseEnv, BehaviorName, BehaviorSpec
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from mlagents.trainers.env_manager import EnvManager, EnvironmentStep, AllStepResult
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from mlagents_envs.timers import timed
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from mlagents.trainers.action_info import ActionInfo
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from mlagents.trainers.settings import ParameterRandomizationSettings
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from mlagents_envs.side_channel.environment_parameters_channel import (
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EnvironmentParametersChannel,
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)
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class SimpleEnvManager(EnvManager):
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"""
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Simple implementation of the EnvManager interface that only handles one BaseEnv at a time.
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This is generally only useful for testing; see SubprocessEnvManager for a production-quality implementation.
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"""
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def __init__(self, env: BaseEnv, env_params: EnvironmentParametersChannel):
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super().__init__()
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self.env_params = env_params
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self.env = env
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self.previous_step: EnvironmentStep = EnvironmentStep.empty(0)
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self.previous_all_action_info: Dict[str, ActionInfo] = {}
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def _step(self) -> List[EnvironmentStep]:
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all_action_info = self._take_step(self.previous_step)
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self.previous_all_action_info = all_action_info
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for brain_name, action_info in all_action_info.items():
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self.env.set_actions(brain_name, action_info.action)
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self.env.step()
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all_step_result = self._generate_all_results()
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step_info = EnvironmentStep(
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all_step_result, 0, self.previous_all_action_info, {}
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)
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self.previous_step = step_info
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return [step_info]
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def _reset_env(
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self, config: Dict[BehaviorName, float] = None
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) -> List[EnvironmentStep]: # type: ignore
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self.set_env_parameters(config)
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self.env.reset()
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all_step_result = self._generate_all_results()
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self.previous_step = EnvironmentStep(all_step_result, 0, {}, {})
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return [self.previous_step]
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def set_env_parameters(self, config: Dict = None) -> None:
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"""
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Sends environment parameter settings to C# via the
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EnvironmentParametersSidehannel.
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:param config: Dict of environment parameter keys and values
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"""
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if config is not None:
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for k, v in config.items():
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if isinstance(v, float):
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self.env_params.set_float_parameter(k, v)
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elif isinstance(v, ParameterRandomizationSettings):
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v.apply(k, self.env_params)
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@property
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def training_behaviors(self) -> Dict[BehaviorName, BehaviorSpec]:
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return self.env.behavior_specs
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def close(self):
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self.env.close()
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@timed
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def _take_step(self, last_step: EnvironmentStep) -> Dict[BehaviorName, ActionInfo]:
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all_action_info: Dict[str, ActionInfo] = {}
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for brain_name, step_tuple in last_step.current_all_step_result.items():
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all_action_info[brain_name] = self.policies[brain_name].get_action(
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step_tuple[0],
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0, # As there is only one worker, we assign the worker_id to 0.
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)
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return all_action_info
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def _generate_all_results(self) -> AllStepResult:
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all_step_result: AllStepResult = {}
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for brain_name in self.env.behavior_specs:
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all_step_result[brain_name] = self.env.get_steps(brain_name)
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return all_step_result
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