highway-env自定义高速路环境

highway-env自定义高速路环境

问题描述

highway-env自车（ego vehicle）初始状态（位置，速度）可以根据给出的API进行设置，但周围车辆（other vehicles）初始状态为随机生成，不可设置（环境开发作者说的，见下图）。

问题测试

import gym
import highway_env

# Create environment
env = gym.make("highway-v0")

env.configure({'observation':{'type':'Kinematics',"absolute":False,"normalize":False},'action':{'type':'DiscreteMetaAction'},'simulation_frequency':15,'policy_frequency':10,'other_vehicles_type':'highway_env.vehicle.behavior.IDMVehicle','screen_width':600,'screen_height':150,'centering_position':[0.3,0.5],'scaling':5.5,'show_trajectories':False,'render_agent':True,'offscreen_rendering':False,'manual_control':False,'real_time_rendering':False,'lanes_count':2,# 车道数'vehicles_count':1,# 周围车辆数'controlled_vehicles':1,'initial_lane_id':None,'duration':100,'ego_spacing':2,'vehicles_density':1,'collision_reward':-1,'right_lane_reward':0.1,'high_speed_reward':0.4,'lane_change_reward':0,'reward_speed_range':[20,30],'offroad_terminal':False,'lane_from':1})
    
eposides =10
rewards =0for eq inrange(eposides):
    obs = env.reset()
    env.render()
    done =Falsewhilenot done:
        action = env.action_space.sample()
        obs, reward, done, info = env.step(action)
        env.render()
        rewards += reward
    print(rewards)

测试视频如下：highway_setting。上述代码中，定义了两辆车：一辆为自车，一辆为周围车辆，想要实现的场景为，两辆车初始在同一条车道，前车速度慢，后车根据驾驶场景，采用强化学习来实现超车然后换回本车道。然后视频可看出，自车初始化在id为1的车道，周围车辆初始化随机出现在两车道中。

依赖包版本

gym == 0.21.0
highway-env == 1.5

单车控制环境

根据上述无法自定义设置周围车辆，解决方案为：按照自车定义方式，然后加入到道路中，但不加入到控制车辆内，这里重写了奖励函数，代码如下：

import highway_env
from highway_env.envs.common.abstract import AbstractEnv
from highway_env.road.road import Road, RoadNetwork
from highway_env.envs.common.action import Action
from highway_env.vehicle.controller import ControlledVehicle
from highway_env.vehicle.kinematics import Vehicle

LANES =2
ANGLE =0
START =0
LENGHT =200
SPEED_LIMIT =30
SPEED_REWARD_RANGE =[10,30]
COL_REWARD =-1
HIGH_SPEED_REWARD =0
RIGHT_LANE_REWARD =0
DURATION =100.0classmyEnv(AbstractEnv):@classmethoddefdefault_config(cls)->dict:
        config =super().default_config()
        config.update({'observation':{'type':'Kinematics',"absolute":False,"normalize":False},'action':{'type':'DiscreteMetaAction'},"reward_speed_range": SPEED_REWARD_RANGE,"simulation_frequency":20,"policy_frequency":20,"centering_position":[0.3,0.5],})return config

    def_reset(self)->None:
        self._create_road()
        self._create_vehicles()def_create_road(self)->None:
        self.road = Road(
            network=RoadNetwork.straight_road_network(LANES, speed_limit=SPEED_LIMIT),
            np_random=self.np_random,
            record_history=False,)# 创建车辆def_create_vehicles(self)->None:

        vehicle = Vehicle.create_random(self.road, speed=23, lane_id=1, spacing=0.3)
        vehicle = self.action_type.vehicle_class(
            self.road,
            vehicle.position,
            vehicle.heading,
            vehicle.speed,)
        self.vehicle = vehicle
        self.road.vehicles.append(vehicle)

        vehicle = Vehicle.create_random(self.road, speed=30, lane_id=1, spacing=0.35)
        vehicle = self.action_type.vehicle_class(
            self.road,
            vehicle.position,
            vehicle.heading,
            vehicle.speed,)
        self.road.vehicles.append(vehicle)# 重写的奖励函数，仅考虑车辆碰撞影响def_reward(self, action: Action)->float:
        reward =0

        lane =(
            self.vehicle.target_lane_index[2]ifisinstance(self.vehicle, ControlledVehicle)else self.vehicle.lane_index[2])if self.vehicle.crashed:
            reward =-1elif lane ==0:
            reward +=1

        reward =0ifnot self.vehicle.on_road else reward

        return reward

    def_is_terminal(self)->bool:return(
            self.vehicle.crashed
            or self.time >= DURATION
            or(Falseandnot self.vehicle.on_road))if __name__ =='__main__':
    env = myEnv()
    obs = env.reset()

    eposides =10
    rewards =0for eq inrange(eposides):
        obs = env.reset()
        env.render()
        done =Falsewhilenot done:
            action = env.action_space.sample()
            obs, reward, done, info = env.step(action)
            env.render()
            rewards += reward
        print(rewards)

测试视频如下：highway_env_single，从视频可看出，两辆车均初始化在同一个车道。但存在颜色没法区分问题，有知道的小伙伴可以留言讨论一下哦。

多车控制环境

import random

import highway_env
from highway_env.envs.common.abstract import AbstractEnv
from highway_env.road.road import Road, RoadNetwork
from highway_env.envs.common.action import Action
from highway_env.vehicle.controller import ControlledVehicle
from highway_env.vehicle.kinematics import Vehicle

LANES =2
ANGLE =0
START =0
LENGHT =200
SPEED_LIMIT =30
SPEED_REWARD_RANGE =[10,30]
COL_REWARD =-1
HIGH_SPEED_REWARD =0
RIGHT_LANE_REWARD =0
DURATION =100.0classmyEnv(AbstractEnv):@classmethoddefdefault_config(cls)->dict:
        config =super().default_config()
        config.update({"observation":{"type":"MultiAgentObservation","observation_config":{"type":"Kinematics","vehicles_count":2,"features":["x","y","vx","vy"],"absolute":True,},},"action":{"type":"MultiAgentAction","action_config":{"type":"DiscreteMetaAction",},},"reward_speed_range": SPEED_REWARD_RANGE,"simulation_frequency":20,"policy_frequency":20,"centering_position":[0.3,0.5],})return config

    def_reset(self)->None:
        self._create_road()
        self._create_vehicles()def_create_road(self)->None:
        self.road = Road(
            network=RoadNetwork.straight_road_network(LANES, speed_limit=SPEED_LIMIT),
            np_random=self.np_random,
            record_history=False,)def_create_vehicles(self)->None:

        self.controlled_vehicles =[]
        vehicle = Vehicle.create_random(self.road, speed=23, lane_id=1, spacing=0.3)
        vehicle = self.action_type.vehicle_class(
            self.road,
            vehicle.position,
            vehicle.heading,
            vehicle.speed,)
        self.controlled_vehicles.append(vehicle)
        self.road.vehicles.append(vehicle)

        vehicle = Vehicle.create_random(self.road, speed=30, lane_id=1, spacing=0.35)
        vehicle = self.action_type.vehicle_class(
            self.road,
            vehicle.position,
            vehicle.heading,
            vehicle.speed,)
        self.controlled_vehicles.append(vehicle)
        self.road.vehicles.append(vehicle)def_reward(self, action: Action)->float:
        reward =0

        lane =(
            self.vehicle.target_lane_index[2]ifisinstance(self.vehicle, ControlledVehicle)else self.vehicle.lane_index[2])if self.vehicle.crashed:
            reward =-1elif lane ==0:
            reward +=1

        reward =0ifnot self.vehicle.on_road else reward

        return reward

    def_is_terminal(self)->bool:return(
            self.vehicle.crashed
            or self.time >= DURATION
            or(Falseandnot self.vehicle.on_road))if __name__ =='__main__':
    env = myEnv()
    obs = env.reset()

    eposides =10
    rewards =0for eq inrange(eposides):
        obs = env.reset()
        env.render()
        done =Falsewhilenot done:# action = env.action_space.sample()
            action1 = random.sample([0,1,2,3,4],1)[0]
            action2 =1
            action =(action1,action2)

            obs, reward, done, info = env.step(action)
            env.render()
            rewards += reward
        print(rewards)

测试视频如下：highway_env_muti

后记

highway-env: 手册