Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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from typing import Any, Dict, List
from mlagents.envs.base_unity_environment import BaseUnityEnvironment
from mlagents.envs.env_manager import EnvManager, EnvironmentStep
from mlagents.envs.timers import timed
from mlagents.envs import ActionInfo, BrainParameters
class SimpleEnvManager(EnvManager):
"""
Simple implementation of the EnvManager interface that only handles one BaseUnityEnvironment at a time.
This is generally only useful for testing; see SubprocessEnvManager for a production-quality implementation.
"""
def __init__(self, env: BaseUnityEnvironment):
super().__init__()
self.env = env
self.previous_step: EnvironmentStep = EnvironmentStep(None, {}, None)
self.previous_all_action_info: Dict[str, ActionInfo] = {}
def step(self) -> List[EnvironmentStep]:
all_action_info = self._take_step(self.previous_step)
self.previous_all_action_info = all_action_info
if self.env.global_done:
all_brain_info = self.env.reset()
else:
actions = {}
memories = {}
texts = {}
values = {}
for brain_name, action_info in all_action_info.items():
actions[brain_name] = action_info.action
memories[brain_name] = action_info.memory
texts[brain_name] = action_info.text
values[brain_name] = action_info.value
all_brain_info = self.env.step(actions, memories, texts, values)
step_brain_info = all_brain_info
step_info = EnvironmentStep(
self.previous_step.current_all_brain_info,
step_brain_info,
self.previous_all_action_info,
)
self.previous_step = step_info
return [step_info]
def reset(
self,
config: Dict[str, float] = None,
train_mode: bool = True,
custom_reset_parameters: Any = None,
) -> List[EnvironmentStep]: # type: ignore
all_brain_info = self.env.reset(
config=config,
train_mode=train_mode,
custom_reset_parameters=custom_reset_parameters,
)
self.previous_step = EnvironmentStep(None, all_brain_info, None)
return [self.previous_step]
@property
def external_brains(self) -> Dict[str, BrainParameters]:
return self.env.external_brains
@property
def reset_parameters(self) -> Dict[str, float]:
return self.env.reset_parameters
def close(self):
self.env.close()
@timed
def _take_step(self, last_step: EnvironmentStep) -> Dict[str, ActionInfo]:
all_action_info: Dict[str, ActionInfo] = {}
for brain_name, brain_info in last_step.current_all_brain_info.items():
all_action_info[brain_name] = self.policies[brain_name].get_action(
brain_info
)
return all_action_info