205 lines
8.4 KiB
Python
205 lines
8.4 KiB
Python
import numpy as np
|
||
from metadrive.component.navigation_module.node_network_navigation import NodeNetworkNavigation
|
||
from metadrive.envs.scenario_env import ScenarioEnv
|
||
from metadrive.component.vehicle.vehicle_type import DefaultVehicle, vehicle_class_to_type
|
||
import math
|
||
import logging
|
||
from collections import defaultdict
|
||
from typing import Union, Dict, AnyStr
|
||
from metadrive.engine.logger import get_logger, set_log_level
|
||
from metadrive.type import MetaDriveType
|
||
|
||
|
||
class PolicyVehicle(DefaultVehicle):
|
||
def __init__(self, *args, **kwargs):
|
||
super().__init__(*args, **kwargs)
|
||
self.policy = None
|
||
self.destination = None
|
||
|
||
def set_policy(self, policy):
|
||
self.policy = policy
|
||
|
||
def set_destination(self, des):
|
||
self.destination = des
|
||
|
||
def act(self, observation, policy=None):
|
||
if self.policy is not None:
|
||
return self.policy.act(observation)
|
||
else:
|
||
return self.action_space.sample()
|
||
|
||
def before_step(self, action):
|
||
self.last_position = self.position # 2D vector
|
||
self.last_velocity = self.velocity # 2D vector
|
||
self.last_speed = self.speed # Scalar
|
||
self.last_heading_dir = self.heading
|
||
if action is not None:
|
||
self.last_current_action.append(action)
|
||
self._set_action(action)
|
||
|
||
def is_done(self):
|
||
# arrive or crash
|
||
pass
|
||
|
||
|
||
vehicle_class_to_type[PolicyVehicle] = "default"
|
||
|
||
|
||
class MultiAgentScenarioEnv(ScenarioEnv):
|
||
@classmethod
|
||
def default_config(cls):
|
||
config = super().default_config()
|
||
config.update(dict(
|
||
data_directory=None,
|
||
num_controlled_agents=3,
|
||
horizon=1000,
|
||
))
|
||
return config
|
||
|
||
def __init__(self, config, agent2policy):
|
||
self.policy = agent2policy
|
||
self.controlled_agents = {}
|
||
self.controlled_agent_ids = []
|
||
self.obs_list = []
|
||
self.round = 0
|
||
super().__init__(config)
|
||
|
||
def reset(self, seed: Union[None, int] = None):
|
||
self.round = 0
|
||
if self.logger is None:
|
||
self.logger = get_logger()
|
||
log_level = self.config.get("log_level", logging.DEBUG if self.config.get("debug", False) else logging.INFO)
|
||
set_log_level(log_level)
|
||
|
||
self.lazy_init()
|
||
self._reset_global_seed(seed)
|
||
if self.engine is None:
|
||
raise ValueError("Broken MetaDrive instance.")
|
||
|
||
# 记录专家数据中每辆车的位置，接着全部清除，只保留位置等信息，用于后续生成
|
||
_obj_to_clean_this_frame = []
|
||
self.car_birth_info_list = []
|
||
for scenario_id, track in self.engine.traffic_manager.current_traffic_data.items():
|
||
if scenario_id == self.engine.traffic_manager.sdc_scenario_id:
|
||
continue
|
||
else:
|
||
if track["type"] == MetaDriveType.VEHICLE:
|
||
_obj_to_clean_this_frame.append(scenario_id)
|
||
valid = track['state']['valid']
|
||
first_show = np.argmax(valid) if valid.any() else -1
|
||
last_show = len(valid) - 1 - np.argmax(valid[::-1]) if valid.any() else -1
|
||
# id，出现时间，出生点坐标，出生朝向，目的地
|
||
self.car_birth_info_list.append({
|
||
'id': track['metadata']['object_id'],
|
||
'show_time': first_show,
|
||
'begin': (track['state']['position'][first_show, 0], track['state']['position'][first_show, 1]),
|
||
'heading': track['state']['heading'][first_show],
|
||
'end': (track['state']['position'][last_show, 0], track['state']['position'][last_show, 1])
|
||
})
|
||
|
||
for scenario_id in _obj_to_clean_this_frame:
|
||
self.engine.traffic_manager.current_traffic_data.pop(scenario_id)
|
||
|
||
self.engine.reset()
|
||
self.reset_sensors()
|
||
self.engine.taskMgr.step()
|
||
|
||
self.lanes = self.engine.map_manager.current_map.road_network.graph
|
||
|
||
if self.top_down_renderer is not None:
|
||
self.top_down_renderer.clear()
|
||
self.engine.top_down_renderer = None
|
||
|
||
self.dones = {}
|
||
self.episode_rewards = defaultdict(float)
|
||
self.episode_lengths = defaultdict(int)
|
||
|
||
self.controlled_agents.clear()
|
||
self.controlled_agent_ids.clear()
|
||
|
||
super().reset(seed) # 初始化场景
|
||
self._spawn_controlled_agents()
|
||
|
||
return self._get_all_obs()
|
||
|
||
def _spawn_controlled_agents(self):
|
||
# ego_vehicle = self.engine.agent_manager.active_agents.get("default_agent")
|
||
# ego_position = ego_vehicle.position if ego_vehicle else np.array([0, 0])
|
||
for car in self.car_birth_info_list:
|
||
if car['show_time'] == self.round:
|
||
agent_id = f"controlled_{car['id']}"
|
||
|
||
vehicle = self.engine.spawn_object(
|
||
PolicyVehicle,
|
||
vehicle_config={},
|
||
position=car['begin'],
|
||
heading=car['heading']
|
||
)
|
||
vehicle.reset(position=car['begin'], heading=car['heading'])
|
||
|
||
vehicle.set_policy(self.policy)
|
||
vehicle.set_destination(car['end'])
|
||
|
||
self.controlled_agents[agent_id] = vehicle
|
||
self.controlled_agent_ids.append(agent_id)
|
||
|
||
# ✅ 关键：注册到引擎的 active_agents，才能参与物理更新
|
||
self.engine.agent_manager.active_agents[agent_id] = vehicle
|
||
|
||
def _get_all_obs(self):
|
||
# position, velocity, heading, lidar, navigation, TODO: trafficlight -> list
|
||
self.obs_list = []
|
||
for agent_id, vehicle in self.controlled_agents.items():
|
||
state = vehicle.get_state()
|
||
|
||
traffic_light = 0
|
||
for lane in self.lanes.values():
|
||
if lane.lane.point_on_lane(state['position'][:2]):
|
||
if self.engine.light_manager.has_traffic_light(lane.lane.index):
|
||
traffic_light = self.engine.light_manager._lane_index_to_obj[lane.lane.index].status
|
||
if traffic_light == 'TRAFFIC_LIGHT_GREEN':
|
||
traffic_light = 1
|
||
elif traffic_light == 'TRAFFIC_LIGHT_YELLOW':
|
||
traffic_light = 2
|
||
elif traffic_light == 'TRAFFIC_LIGHT_RED':
|
||
traffic_light = 3
|
||
else:
|
||
traffic_light = 0
|
||
break
|
||
|
||
lidar = self.engine.get_sensor("lidar").perceive(num_lasers=80, distance=30, base_vehicle=vehicle,
|
||
physics_world=self.engine.physics_world.dynamic_world)
|
||
side_lidar = self.engine.get_sensor("side_detector").perceive(num_lasers=10, distance=8,
|
||
base_vehicle=vehicle,
|
||
physics_world=self.engine.physics_world.static_world)
|
||
lane_line_lidar = self.engine.get_sensor("lane_line_detector").perceive(num_lasers=10, distance=3,
|
||
base_vehicle=vehicle,
|
||
physics_world=self.engine.physics_world.static_world)
|
||
|
||
obs = (state['position'][:2] + list(state['velocity']) + [state['heading_theta']]
|
||
+ lidar[0] + side_lidar[0] + lane_line_lidar[0] + [traffic_light]
|
||
+ list(vehicle.destination))
|
||
self.obs_list.append(obs)
|
||
return self.obs_list
|
||
|
||
def step(self, action_dict: Dict[AnyStr, Union[list, np.ndarray]]):
|
||
self.round += 1
|
||
|
||
for agent_id, action in action_dict.items():
|
||
if agent_id in self.controlled_agents:
|
||
self.controlled_agents[agent_id].before_step(action)
|
||
|
||
self.engine.step()
|
||
|
||
for agent_id in action_dict:
|
||
if agent_id in self.controlled_agents:
|
||
self.controlled_agents[agent_id].after_step()
|
||
|
||
self._spawn_controlled_agents()
|
||
obs = self._get_all_obs()
|
||
rewards = {aid: 0.0 for aid in self.controlled_agents}
|
||
dones = {aid: False for aid in self.controlled_agents}
|
||
dones["__all__"] = self.episode_step >= self.config["horizon"]
|
||
infos = {aid: {} for aid in self.controlled_agents}
|
||
return obs, rewards, dones, infos
|