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75 行
2.6 KiB
75 行
2.6 KiB
from typing import Any, Dict, List
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from mlagents.envs.base_unity_environment import BaseUnityEnvironment
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from mlagents.envs.env_manager import EnvManager, EnvironmentStep
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from mlagents.envs.timers import timed
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from mlagents.envs.action_info import ActionInfo
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from mlagents.envs.brain import BrainParameters
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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 BaseUnityEnvironment 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: BaseUnityEnvironment):
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super().__init__()
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self.env = env
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self.previous_step: EnvironmentStep = EnvironmentStep(None, {}, None)
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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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actions = {}
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values = {}
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for brain_name, action_info in all_action_info.items():
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actions[brain_name] = action_info.action
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values[brain_name] = action_info.value
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all_brain_info = self.env.step(vector_action=actions, value=values)
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step_brain_info = all_brain_info
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step_info = EnvironmentStep(
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self.previous_step.current_all_brain_info,
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step_brain_info,
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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(
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self,
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config: Dict[str, float] = None,
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train_mode: bool = True,
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custom_reset_parameters: Any = None,
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) -> List[EnvironmentStep]: # type: ignore
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all_brain_info = self.env.reset(
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config=config,
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train_mode=train_mode,
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custom_reset_parameters=custom_reset_parameters,
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)
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self.previous_step = EnvironmentStep(None, all_brain_info, None)
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return [self.previous_step]
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@property
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def external_brains(self) -> Dict[str, BrainParameters]:
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return self.env.external_brains
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@property
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def reset_parameters(self) -> Dict[str, float]:
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return self.env.reset_parameters
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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[str, ActionInfo]:
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all_action_info: Dict[str, ActionInfo] = {}
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for brain_name, brain_info in last_step.current_all_brain_info.items():
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all_action_info[brain_name] = self.policies[brain_name].get_action(
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brain_info
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)
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return all_action_info
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