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 Dict, List
from mlagents_envs.base_env import BaseEnv, AgentGroup
from mlagents.trainers.env_manager import EnvManager, EnvironmentStep, AllStepResult
from mlagents_envs.timers import timed
from mlagents.trainers.action_info import ActionInfo
from mlagents.trainers.brain import BrainParameters
from mlagents_envs.side_channel.float_properties_channel import FloatPropertiesChannel
from mlagents.trainers.brain_conversion_utils import group_spec_to_brain_parameters
class SimpleEnvManager(EnvManager):
"""
Simple implementation of the EnvManager interface that only handles one BaseEnv at a time.
This is generally only useful for testing; see SubprocessEnvManager for a production-quality implementation.
"""
def __init__(self, env: BaseEnv, float_prop_channel: FloatPropertiesChannel):
super().__init__()
self.shared_float_properties = float_prop_channel
self.env = env
self.previous_step: EnvironmentStep = EnvironmentStep.empty(0)
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
for brain_name, action_info in all_action_info.items():
self.env.set_actions(brain_name, action_info.action)
self.env.step()
all_step_result = self._generate_all_results()
step_info = EnvironmentStep(all_step_result, 0, self.previous_all_action_info)
self.previous_step = step_info
return [step_info]
def reset(
self, config: Dict[AgentGroup, float] = None
) -> List[EnvironmentStep]: # type: ignore
if config is not None:
for k, v in config.items():
self.shared_float_properties.set_property(k, v)
self.env.reset()
all_step_result = self._generate_all_results()
self.previous_step = EnvironmentStep(all_step_result, 0, {})
return [self.previous_step]
@property
def external_brains(self) -> Dict[AgentGroup, BrainParameters]:
result = {}
for brain_name in self.env.get_agent_groups():
result[brain_name] = group_spec_to_brain_parameters(
brain_name, self.env.get_agent_group_spec(brain_name)
)
return result
@property
def get_properties(self) -> Dict[AgentGroup, float]:
return self.shared_float_properties.get_property_dict_copy()
def close(self):
self.env.close()
@timed
def _take_step(self, last_step: EnvironmentStep) -> Dict[AgentGroup, ActionInfo]:
all_action_info: Dict[str, ActionInfo] = {}
for brain_name, step_info in last_step.current_all_step_result.items():
all_action_info[brain_name] = self.policies[brain_name].get_action(
step_info,
0, # As there is only one worker, we assign the worker_id to 0.
)
return all_action_info
def _generate_all_results(self) -> AllStepResult:
all_step_result: AllStepResult = {}
for brain_name in self.env.get_agent_groups():
all_step_result[brain_name] = self.env.get_step_result(brain_name)
return all_step_result