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111 行
4.1 KiB
111 行
4.1 KiB
from abc import ABC, abstractmethod
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import logging
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from typing import List, Dict, NamedTuple, Iterable
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from mlagents_envs.base_env import BatchedStepResult, AgentGroupSpec, AgentGroup
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from mlagents.trainers.brain import BrainParameters
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from mlagents.trainers.tf_policy import TFPolicy
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from mlagents.trainers.agent_processor import AgentManager, AgentManagerQueue
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from mlagents.trainers.action_info import ActionInfo
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AllStepResult = Dict[AgentGroup, BatchedStepResult]
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AllGroupSpec = Dict[AgentGroup, AgentGroupSpec]
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logger = logging.getLogger("mlagents.trainers")
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class EnvironmentStep(NamedTuple):
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current_all_step_result: AllStepResult
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worker_id: int
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brain_name_to_action_info: Dict[AgentGroup, ActionInfo]
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@property
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def name_behavior_ids(self) -> Iterable[AgentGroup]:
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return self.current_all_step_result.keys()
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@staticmethod
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def empty(worker_id: int) -> "EnvironmentStep":
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return EnvironmentStep({}, worker_id, {})
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class EnvManager(ABC):
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def __init__(self):
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self.policies: Dict[AgentGroup, TFPolicy] = {}
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self.agent_managers: Dict[AgentGroup, AgentManager] = {}
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self.first_step_infos: List[EnvironmentStep] = None
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def set_policy(self, brain_name: AgentGroup, policy: TFPolicy) -> None:
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self.policies[brain_name] = policy
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if brain_name in self.agent_managers:
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self.agent_managers[brain_name].policy = policy
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def set_agent_manager(self, brain_name: AgentGroup, manager: AgentManager) -> None:
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self.agent_managers[brain_name] = manager
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@abstractmethod
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def _step(self) -> List[EnvironmentStep]:
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pass
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@abstractmethod
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def _reset_env(self, config: Dict = None) -> List[EnvironmentStep]:
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pass
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def reset(self, config: Dict = None) -> int:
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for manager in self.agent_managers.values():
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manager.end_episode()
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# Save the first step infos, after the reset.
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# They will be processed on the first advance().
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self.first_step_infos = self._reset_env(config)
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return len(self.first_step_infos)
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@property
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@abstractmethod
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def external_brains(self) -> Dict[AgentGroup, BrainParameters]:
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pass
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@property
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@abstractmethod
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def get_properties(self) -> Dict[AgentGroup, float]:
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pass
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@abstractmethod
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def close(self):
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pass
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def advance(self):
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# If we had just reset, process the first EnvironmentSteps.
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# Note that we do it here instead of in reset() so that on the very first reset(),
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# we can create the needed AgentManagers before calling advance() and processing the EnvironmentSteps.
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if self.first_step_infos is not None:
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self._process_step_infos(self.first_step_infos)
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self.first_step_infos = None
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# Get new policies if found
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for brain_name in self.external_brains:
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try:
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_policy = self.agent_managers[brain_name].policy_queue.get_nowait()
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self.set_policy(brain_name, _policy)
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except AgentManagerQueue.Empty:
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pass
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# Step the environment
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new_step_infos = self._step()
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# Add to AgentProcessor
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num_step_infos = self._process_step_infos(new_step_infos)
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return num_step_infos
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def _process_step_infos(self, step_infos: List[EnvironmentStep]) -> int:
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for step_info in step_infos:
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for name_behavior_id in step_info.name_behavior_ids:
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if name_behavior_id not in self.agent_managers:
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logger.warning(
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"Agent manager was not created for behavior id {}.".format(
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name_behavior_id
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)
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)
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continue
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self.agent_managers[name_behavior_id].add_experiences(
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step_info.current_all_step_result[name_behavior_id],
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step_info.worker_id,
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step_info.brain_name_to_action_info.get(
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name_behavior_id, ActionInfo.empty()
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),
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)
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return len(step_infos)
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