我是靠谱客的博主 鳗鱼小白菜,最近开发中收集的这篇文章主要介绍CARLA平台+Q-learning的尝试(gym-carla),觉得挺不错的,现在分享给大家,希望可以做个参考。

概述

接触强化学习大概有半年了,也了解了一些算法,一些简单的算法在gym框架也实现了,那么结合仿真平台Carla该怎么用呢?由于比较熟悉gym框架,就偷个懒先从这个开始写代码。

项目地址:https://github.com/cjy1992/gym-carla

文章目录

    • 一、环境配置
      • 1.1 基本配置
      • 1.2 配置工作环境
      • 1.3 运行测试
    • 二、环境解读
      • 2.1 test.py--超参数设置
      • 2.2 环境介绍
        • 2.2.1 动作空间
        • 2.2.2 状态空间
        • 2.2.3 test.py主函数
        • 2.3.4 Q-learning
        • 2.3.5 实验结果

一、环境配置

1.1 基本配置

  • Ubuntu 16.04(作者在Ubuntu 20.04测试成功)
  • Anaconda
  • Carla 0.96

1.2 配置工作环境

  • 建立pyhon3.6的环境
$ conda create -n py36 python=3.6
  • git代码
$ git clone https://github.com/cjy1992/gym-carla.git
  • 进入该路径,cd XXX
$ pip install -r requiremtns.txt
$ pip install -e .
  • 下载Carla0.9.6版本

    https://github.com/carla-simulator/carla/releases/tag/0.9.6

  • 添加环境变量(否则会出现No module named 'carla'

    • 方法一:

      $ conda activate py36
      $ export PYTHONPATH=$PYTHONPATH:/home/shy/CARLA_0.9.6/PythonAPI/carla/dist/carla-0.9.6-py3.5-linux-x86_64.egg
      
    • 方法二:在环境变量配置文件中添加上述环境变量

    • 方法三:安装easy_install后,easy_install XXX.egg即可

    • 方法四:在主函数代码里添加这句即可

      import sys
      try:
      sys.path.append('/home/shy/CARLA_0.9.6/PythonAPI/carla/dist/carla-0.9.6-py3.5-linux-x86_64.egg')
      

      方法五:https://github.com/carla-simulator/carla/issues/1466

1.3 运行测试

  • 打开CARLA的目录,右键终端启动Carla
$ ./CarlaUE4.sh -windowed -carla-port=2000

也可以启动无界面模式(提高运行速度)

$ DISPLAY= ./CarlaUE4.sh -opengl -carla-port=2000
  • 打开该项目的目录,右键终端输入python test.py

二、环境解读

2.1 test.py–超参数设置

#!/usr/bin/env python
# Copyright (c) 2019: Jianyu Chen (jianyuchen@berkeley.edu).
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
import gym
import sys
try:
sys.path.append('/home/shy/CARLA_0.9.6/PythonAPI/carla/dist/carla-0.9.6-py3.5-linux-x86_64.egg') # 手动添加环境变量
except IndexError:
pass
import gym_carla
import carla
def main():
# parameters for the gym_carla environment
params = {
'number_of_vehicles': 100,
'number_of_walkers': 0,
'display_size': 512,
# screen size of bird-eye render
'max_past_step': 1,
# the number of past steps to draw
'dt': 0.1,
# time interval between two frames
'discrete': False,
# whether to use discrete control space
'discrete_acc': [-3.0, 0.0, 3.0],
# discrete value of accelerations
'discrete_steer': [-0.2, 0.0, 0.2],
# discrete value of steering angles
'continuous_accel_range': [-3.0, 3.0],
# continuous acceleration range
'continuous_steer_range': [-0.3, 0.3],
# continuous steering angle range
'ego_vehicle_filter': 'vehicle.lincoln*',
# filter for defining ego vehicle
'port': 2000,
# connection port
'town': 'Town03',
# which town to simulate
'task_mode': 'random',
# mode of the task, [random, roundabout (only for Town03)]
'max_time_episode': 1000,
# maximum timesteps per episode
'max_waypt': 12,
# maximum number of waypoints
'obs_range': 32,
# observation range (meter)
'lidar_bin': 0.125,
# bin size of lidar sensor (meter)
'd_behind': 12,
# distance behind the ego vehicle (meter)
'out_lane_thres': 2.0,
# threshold for out of lane
'desired_speed': 30,
# desired speed (m/s)
'max_ego_spawn_times': 200,
# maximum times to spawn ego vehicle
'display_route': True,
# whether to render the desired route
'pixor_size': 64,
# size of the pixor labels
'pixor': False,
# whether to output PIXOR observation
}

2.2 环境介绍

2.2.1 动作空间

包括两个动作,分别是油门方向,并且有离散和连续两种情况。

  • 离散空间:
'discrete_acc': [-3.0, 0.0, 3.0],
# discrete value of accelerations
'discrete_steer': [-0.2, 0.0, 0.2],
# discrete value of steering angles
  • 连续空间
'continuous_accel_range': [-3.0, 3.0],
# continuous acceleration range
'continuous_steer_range': [-0.3, 0.3],
# continuous steering angle range

2.2.2 状态空间

包括四个部分,分别是鸟瞰图雷达相机,以及车辆的当前状态。

如果输出它们的维度,则为:

# BIRDEYE shape is (256, 256, 3)
# LIDAR shape is (256, 256, 3)
# CAMERA shape is (256, 256, 3)
# STATE shape is (4,)

状态的代码如下,分别表示与车道线的横向距离与车道线的夹角当前速度和前方车辆的距离


# State observation
ego_trans = self.ego.get_transform()
ego_x = ego_trans.location.x
ego_y = ego_trans.location.y
ego_yaw = ego_trans.rotation.yaw/180*np.pi
lateral_dis, w = get_preview_lane_dis(self.waypoints, ego_x, ego_y)
delta_yaw = np.arcsin(np.cross(w,
np.array(np.array([np.cos(ego_yaw), np.sin(ego_yaw)]))))
v = self.ego.get_velocity()
speed = np.sqrt(v.x**2 + v.y**2)
state = np.array([lateral_dis, - delta_yaw, speed, self.vehicle_front])

2.2.3 test.py主函数


# Set gym-carla environment
env = gym.make('carla-v0', params=params)
obs = env.reset() # 重置环境
while True:
action = [2.0, 0.0] #此时只执行前进动作,没有转向动作
obs,r,done,info = env.step(action) #状态,奖励,是否完成,info,done=False
if done: #如果这个episode结束了,done=True,比如碰撞了压过车道线多少了等情况
obs = env.reset() # 重置环境
if __name__ == '__main__':
main()

如果试着写一个Q-learning的话,框架是什么样的呢?显然,这里的obs应该选取obs[states],前三个状态图像适合做深度网络,比如图片输入到CNN这种,结合类似DQN的方法,第四个状态包含了4个信息,与车道线的横向距离与车道线的夹角当前速度和前方车辆的距离
为了方便写代码,就选取了一个单独的状态,做一个Q-table

2.3.4 Q-learning

  • 动作空间,选取离散空间,即动作为[acc,steer],一共9种组合。
  • 进一步建立函数action输入 0 − 8 0-8 08,则选择对应离散的9个动作。
'discrete_acc': [-3.0, 0.0, 3.0],
# discrete value of accelerations
'discrete_steer': [-0.2, 0.0, 0.2],
# discrete value of steering angles
  • 状态空间,采取自身的前两个状态,即obs[states][0],分别表示和车道线的距离。由于距离可能为负数和小数,那么就直接取整处理了,并且给一个number。
  • 创建一个Q值表,维度为 9 × 9 9times 9 9×9
#!/usr/bin/env python
# Copyright (c) 2019: Jianyu Chen (jianyuchen@berkeley.edu).
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
import gym
import sys
import numpy as np
import matplotlib.pyplot as plt
try:
sys.path.append('/home/shy/CARLA_0.9.6/PythonAPI/carla/dist/carla-0.9.6-py3.5-linux-x86_64.egg')
except IndexError:
pass
import gym_carla
import carla
def select_action(action_number):
if action_number == 0:
real_action = [1, -0.2]
elif action_number == 1:
real_action = [1, 0]
elif action_number == 2:
real_action = [1, 0.2]
elif action_number == 3:
real_action = [2, -0.2]
elif action_number == 4:
real_action = [2, 0]
elif action_number == 5:
real_action = [2, 0.2]
elif action_number == 6:
real_action = [3.0, -0.2]
elif action_number == 7:
real_action = [3.0, 0]
elif action_number == 8:
real_action = [3.0, 0.2]
return real_action
def discrete_state(obs):
distance = np.floor(obs['state'][0])
if distance < -3:
distance_number = 0
elif distance == -3:
distance_number = 1
elif distance == -2:
distance_number = 2
elif distance == -1:
distance_number = 3
elif distance == 0:
distance_number = 4
elif distance == 1:
distance_number = 5
elif distance == 2:
distance_number = 6
elif distance == 3:
distance_number = 7
else:
distance_number = 8
return distance_number
def main():
# parameters for the gym_carla environment
params = {
'number_of_vehicles': 100,
'number_of_walkers': 0,
'display_size': 512,
# screen size of bird-eye render
'max_past_step': 1,
# the number of past steps to draw
'dt': 0.1,
# time interval between two frames
'discrete': False,
# whether to use discrete control space
'discrete_acc': [-3.0, 0.0, 3.0],
# discrete value of accelerations
'discrete_steer': [-0.2, 0.0, 0.2],
# discrete value of steering angles
'continuous_accel_range': [-3.0, 3.0],
# continuous acceleration range
'continuous_steer_range': [-0.3, 0.3],
# continuous steering angle range
'ego_vehicle_filter': 'vehicle.lincoln*',
# filter for defining ego vehicle
'port': 2000,
# connection port
'town': 'Town03',
# which town to simulate
'task_mode': 'random',
# mode of the task, [random, roundabout (only for Town03)]
'max_time_episode': 1000,
# maximum timesteps per episode
'max_waypt': 12,
# maximum number of waypoints
'obs_range': 32,
# observation range (meter)
'lidar_bin': 0.125,
# bin size of lidar sensor (meter)
'd_behind': 12,
# distance behind the ego vehicle (meter)
'out_lane_thres': 2.0,
# threshold for out of lane
'desired_speed': 10,
# desired speed (m/s)
'max_ego_spawn_times': 200,
# maximum times to spawn ego vehicle
'display_route': True,
# whether to render the desired route
'pixor_size': 64,
# size of the pixor labels
'pixor': False,
# whether to output PIXOR observation
'learning_rate': 0.1,
'discount': 0.9,
'epsilon': 0.8,
}
env = gym.make('carla-v0', params=params)
env.reset()
q_table = np.zeros([9, 9])
# 创建一个空的Q值表
action_number = 7
# 选择初始动作为油门,不转向
reward_list = []
for episode in range(10000):
if np.random.random() > params['epsilon']:
action = select_action(action_number)
else:
action = select_action(np.random.randint(0, 8))
print("# Episode{} start!".format(episode))
print("choose_action ", action)
obs, reward, done, info = env.step(action)
# 根据初始动作观察环境状态,此时done=False
reward_list.append(reward)
s = discrete_state(obs)
print("# the reward is", reward)
print("# the state distance is", s)
if not done:
max_future_q = np.max(q_table[s, :])
q_table[s, action_number] = (1 - params["learning_rate"]) * q_table[s, action_number] + params[
"learning_rate"] * (reward + params["discount"] * max_future_q)
action_number = np.argmax(q_table[s, :])
print("new_action number",action_number)
else:
env.reset()
return q_table, reward_list
if __name__ == '__main__':
q_table, reward_list = main()
print(q_table)

操作后,可以选择np.save保存这个表,然后下次直接np.load这个初始表,替换np.zeros那个表即可。

2.3.5 实验结果

非常烂!奖励函数的话波动非常剧烈,不太好。
不过可以明显看出来随着episode的增加,似乎比开始的策略走的好一些。
一个环境的初始策略演示视频
有时候也会出现一个bug,意思是传感器还没有销毁。看对应的代码中reset()函数中销毁了,应该是速度太快了,还没有销毁完下一次生成就来了,可以尝试sleep(0.1),或者重新启动这个程序,也可以每次的episode设置小一些,然后用训练好的表继续读进去反复训练。
就这样,下一次尝试写一个DQN,继续熟悉环境!

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