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mainRUN.py
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import torch
import os
import sys
from ReplayBuffer import ReplayBuffer
from memeory import Memory
from SumoAgent import SumoAgent
from genCar import GenCar
from agent import DQNAgent
from plots import mplot, record_data, IndSummaryPlot
from fixedTime.ft_controller import ft_control,max_pressure
from dqn import dqn_control
MAX_EPISODES = 50
MAX_STEPS = 7200
if 'SUMO_HOME' in os.environ:
tools = os.path.join(os.environ['SUMO_HOME'], 'tools')
sys.path.append(tools)
# 创建一个sumo agent
# 奖励的权重
c1, c2, c3 = 1, 0, 0
sumofile = './sumoNet/net.sumocfg'
port = 5905
inEdges = ['gneE1', 'gneE3', 'gneE0', 'gneE2']
outEdges = ['-gneE1', '-gneE3', '-gneE0', '-gneE2']
inLanes = ['gneE1_0', 'gneE1_1', 'gneE1_2',
'gneE3_0', 'gneE3_1', 'gneE3_2',
'gneE0_0', 'gneE0_1', 'gneE0_2',
'gneE2_0', 'gneE2_1', 'gneE2_2']
outLanes = ['-gneE1_0', '-gneE1_1', '-gneE1_2',
'-gneE3_0', '-gneE3_1', '-gneE3_2',
'-gneE0_0', '-gneE0_1', '-gneE0_2',
'-gneE2_0', '-gneE2_1', '-gneE2_2']
sumoAgent = SumoAgent(sumofile, port, inEdges, outEdges, inLanes, outLanes, sumoBinary='sumo-gui')
# 创建一个replayBuffer对象
capacity, minibatch = 30000, 2000
replayBuffer = ReplayBuffer(capacity, minibatch)
# memory = Memory(capacity,minibatch)
# 创建一个车辆生成器
totalNumber, maxSteps, leftTurn, straight, fileName, scale = 4000, MAX_STEPS, 4, 4, "./sumoNet/test3.rou.xml", 1
gencar = GenCar(totalNumber, maxSteps, leftTurn, straight, fileName, scale)
# gencar.gen_dynamic_demand()
# gencar.generate_routefile()
# 初始化一个agent
in_dim, mid_dim, out_dim = 12, 3 * 9, 4
learning_rate, gamma, epsilon, alpha = 0.001, 0.95, 0.9, 0.3 # decay大概19轮左右
agent = DQNAgent(in_dim, mid_dim, out_dim, replayBuffer, learning_rate, gamma, epsilon, alpha, sumoAgent)
C = 5
net_update_times = 0
# dqn 数据记录
queue_list, delay_list, travel_list, reward_list = [], [], [], []
steps_list,per_reward_list, per_queue_list, per_delay_list, per_travel_list = [], [], [], [], []
# ft 数据记录
ft_queue_list, ft_delay_list, ft_travel_list = [], [], []
ft_steps,ft_per_queue_list, ft_per_travel_list, ft_per_delay_list = [], [], [], []
# mp 数据记录
mp_queue_list, mp_delay_list, mp_travel_list = [], [], []
mp_steps, mp_per_queue_list, mp_per_delay_list, mp_per_travel_list = [], [], [], []
for episode in range(MAX_EPISODES):
# 动态生成车辆
gencar.gen_dynamic_demand()
gencar.generate_routefile()
print("episode ", episode)
ft_queues, ft_delays, ft_travels, ft_per_queue, ft_per_delay, ft_per_travel_time,ft_step = ft_control(sumoAgent,
sumofile,
port, inEdges,
outEdges, inLanes,
outLanes, totalNumber,MAX_STEPS)
ft_queue_list.append(ft_queues)
ft_delay_list.append(ft_delays)
ft_travel_list.append(ft_travels)
ft_per_queue_list.append(ft_per_queue)
ft_per_delay_list.append(ft_per_delay)
ft_per_travel_list.append(ft_per_travel_time)
ft_steps.append(ft_step)
print('ft finished')
mp_sumofile = './sumoNet/net2.sumocfg'
mp_queues, mp_delays, mp_travels, mp_per_queue, mp_per_delay, mp_per_travel_time,mp_step = max_pressure(sumoAgent, mp_sumofile, port, inEdges,
outEdges, inLanes, outLanes,
totalNumber,
MAX_STEPS)
mp_queue_list.append(mp_queues)
mp_delay_list.append(mp_delays)
mp_travel_list.append(mp_travels)
mp_per_queue_list.append(mp_per_queue)
mp_per_delay_list.append(mp_per_delay)
mp_per_travel_list.append(mp_per_travel_time)
mp_steps.append(mp_step)
print("mp finished")
queues, delays, travel_times, rewards, per_queue, per_delay, per_travel, per_reward,steps = dqn_control(sumoAgent,
sumofile, port,
inEdges, outEdges,
inLanes, outLanes,
totalNumber,
agent,
MAX_EPISODES,
MAX_STEPS, in_dim,
net_update_times,
C,
replayBuffer)
queue_list.append(queues)
delay_list.append(delays)
travel_list.append(travel_times)
reward_list.append(rewards)
per_reward_list.append(per_reward)
per_queue_list.append(per_queue)
per_delay_list.append(per_delay)
per_travel_list.append(per_travel)
steps_list.append(steps)
print('dqn finished')
# 每轮都记录一次数据 避免某次出现错误丧失数据！
N = 6
fname = 'data_save/data_{0}.xlsx'.format(N)
data_list = {'rewards': reward_list,
'delay_time': delay_list,
'queue': queue_list,
'travel_time': travel_list,
'per_reward': per_reward_list,
'per_delay_time': per_delay_list,
'per_queue': per_queue_list,
'per_travel_time': per_travel_list}
record_data(fname, data_list)
N = 6
# 绘图
mplot(per_reward_list, 'per_reward', N)
# mplot(per_queue_list, 'queues', N)
# mplot(per_delay_list, 'delays', N)
# mplot(ft_per_queue_list, 'ft_queues', N)
# mplot(ft_per_delay_list, 'ft_delays', N)
# mplot(ft_travel_list, 'ft_travel_time', N)
IndSummaryPlot(N, 'queue', 'delay', 'travel', ft=[ft_per_queue_list, ft_per_delay_list, ft_per_travel_list],
dqn=[per_queue_list, per_delay_list, per_travel_list],
mp=[mp_per_queue_list, mp_per_delay_list, mp_per_travel_list])
# 将数据存入excel
fname = 'data_save/data_{0}.xlsx'.format(N)
data_list = {'rewards': reward_list,
'delay_time': delay_list,
'queue': queue_list,
'travel_time': travel_list,
'per_reward': per_reward_list,
'per_delay_time': per_delay_list,
'per_queue': per_queue_list,
'per_travel_time': per_travel_list,
"steps": steps_list
}
record_data(fname, data_list)
fname2 = 'data_save/ft_data_{0}.xlsx'.format(N)
data_list2 = {'ft_delay_time': ft_delay_list,
'ft_queue': ft_queue_list,
'ft_travel_time': ft_travel_list,
'ft_per_delay_time': ft_per_delay_list,
'ft_per_queue': ft_per_queue_list,
'ft_per_travel_time': ft_per_travel_list,
'ft_steps':ft_steps
}
record_data(fname2, data_list2)
fname3 = 'data_save/mp_data_{0}.xlsx'.format(N)
data_list3 = {'mp_delay_time': mp_delay_list,
'mp_queue': mp_queue_list,
'mp_travel_time': mp_travel_list,
'mp_per_delay_time': mp_per_delay_list,
'mp_per_queue': mp_per_queue_list,
'mp_per_travel_time': mp_per_travel_list,
'mp_steps': mp_steps
}
record_data(fname3, data_list3)