-
Notifications
You must be signed in to change notification settings - Fork 0
/
(new)repeatedly_train.py
358 lines (313 loc) · 15.8 KB
/
(new)repeatedly_train.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
from utils import cross_loss_curve, GAMA_connect,reset,send_to_GAMA
from CV_input import generate_img,generate_img_train
import os
from itertools import count
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import MultivariateNormal
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
device = torch.device("cuda" if torch.cuda.is_available() else"cpu")
save_curve_pic = os.path.abspath(os.curdir)+'/Generate_Traffic_Flow_MAS_RL/result/Actor_Critic_3loss_curve.png'
save_critic_loss = os.path.abspath(os.curdir)+'/Generate_Traffic_Flow_MAS_RL/training_data/AC_critic_3loss.csv'
save_reward = os.path.abspath(os.curdir)+'/Generate_Traffic_Flow_MAS_RL/training_data/AC_3reward.csv'
save_speed = os.path.abspath(os.curdir)+'/Generate_Traffic_Flow_MAS_RL/training_data/AC_average_speed.csv'
save_NPC_speed = os.path.abspath(os.curdir)+'/Generate_Traffic_Flow_MAS_RL/training_data/NPC_speed.csv'
state_size = 3
action_size = 1
torch.set_default_tensor_type(torch.DoubleTensor)
class Memory:
def __init__(self):
self.actions = []
self.states = []
self.states_next = []
self.states_img = []
self.states_img_next = []
self.logprobs = []
self.rewards = []
self.is_terminals = []
def clear_memory(self):
del self.actions[:]
del self.states[:]
del self.states_next[:]
del self.states_img[:]
del self.states_img_next[:]
del self.logprobs[:]
del self.rewards[:]
del self.is_terminals[:]
class Actor(nn.Module):
def __init__(self, state_size, action_size):
super(Actor, self).__init__()
self.conv1 = nn.Conv2d(3,8, kernel_size=8, stride=4, padding=0) # 500*500*3 -> 124*124*8
self.maxp1 = nn.MaxPool2d(4, stride = 2, padding=0) # 124*124*8 -> 61*61*8
self.conv2 = nn.Conv2d(8, 16, kernel_size=4, stride=1, padding=0) # 61*61*8 -> 58*58*16
self.maxp2 = nn.MaxPool2d(2, stride=2, padding=0) # 58*58*16 -> 29*29*16 = 13456
self.linear_CNN_1 = nn.Linear(13456, 256)
self.linear_CNN_2 = nn.Linear(768,256)
#
self.state_size = state_size
self.action_size = action_size
self.linear1 = nn.Linear(self.state_size, 128)
self.linear2 = nn.Linear(128, 85)
self.linear3 = nn.Linear(511,128)
self.linear4 = nn.Linear(128,32)
self.mu = nn.Linear(32,self.action_size) #256 linear2
self.sigma = nn.Linear(32,self.action_size)
self.hidden_cell = (torch.zeros(1,1,64).to(device),
torch.zeros(1,1,64).to(device))
def forward(self, state,tensor_cv):
# CV
x = F.relu(self.maxp1(self.conv1(tensor_cv)))
x = F.relu(self.maxp2(self.conv2(x)))
x = x.view(x.size(0), -1) #展開
x = F.relu(self.linear_CNN_1(x)).reshape(1,768)
x = F.relu(self.linear_CNN_2(x)).reshape(1,256)
# num
output_1 = F.relu(self.linear1(state))
output_2 = F.relu(self.linear2(output_1)).reshape(1,255)
# merge
output_2 = torch.cat((x,output_2),1)
output_3 = F.relu(self.linear3(output_2) )
#
output_4 =F.relu(self.linear4(output_3)) #F.relu(self.linear4(output_3.view(-1,c))) #
mu = torch.tanh(self.mu(output_4)) #有正有负 sigmoid 0-1
sigma = F.relu(self.sigma(output_4)) + 0.001
mu = torch.diag_embed(mu).to(device)
sigma = torch.diag_embed(sigma).to(device) # change to 2D
dist = MultivariateNormal(mu,sigma) #N(μ，σ^2)
entropy = dist.entropy().mean()
action = dist.sample()
action_logprob = dist.log_prob(action)
action = torch.clamp(action.detach(), -0.8, 0.6)
return action,action_logprob
class Critic(nn.Module):
def __init__(self, state_size, action_size):
super(Critic, self).__init__()
self.conv1 = nn.Conv2d(3,8, kernel_size=8, stride=4, padding=0) # 500*500*3 -> 124*124*8
self.maxp1 = nn.MaxPool2d(4, stride = 2, padding=0) # 124*124*8 -> 61*61*8
self.conv2 = nn.Conv2d(8, 16, kernel_size=4, stride=1, padding=0) # 61*61*8 -> 58*58*16
self.maxp2 = nn.MaxPool2d(2, stride=2, padding=0) # 58*58*16 -> 29*29*16 = 13456
self.linear_CNN = nn.Linear(13456, 256)
self.lstm_CNN = nn.LSTM(256,85, 1,batch_first=True) #768,255
#
self.state_size = state_size
self.action_size = action_size
self.linear1 = nn.Linear(self.state_size, 128)
self.linear2 = nn.Linear(128, 128)
self.lstm3 = nn.LSTM(128,85,1, batch_first=True)
#
self.LSTM_layer_3 = nn.LSTM(510,128,1, batch_first=True)
self.linear4 = nn.Linear(128,32) #
self.linear5 = nn.Linear(32, action_size)
def forward(self, state, tensor_cv,h_state_cv_c=(torch.zeros(1,3,85).to(device),
torch.zeros(1,3,85).to(device)),h_state_n_c=(torch.zeros(1,3,85).to(device),
torch.zeros(1,3,85).to(device)),h_state_3_c=(torch.zeros(1,1,128).to(device),
torch.zeros(1,1,128).to(device))):
# CV
x = F.relu(self.maxp1(self.conv1(tensor_cv)))
x = F.relu(self.maxp2(self.conv2(x))).reshape(3,1,13456)
#x = x.view(x.size(0), -1) #展開
x = F.relu(self.linear_CNN(x))#.reshape(1,768)
x,h_state_cv_c = self.lstm_CNN(x ,h_state_cv_c) # #.unsqueeze(0)
x = F.relu( x).reshape(1,255) #torch.tanh
# num
output_1 = F.relu(self.linear1(state))
output_2 = F.relu(self.linear2(output_1))
output_2,h_state_n_c = self.lstm3(output_2,h_state_n_c) #
output_2 = F.relu(output_2) #
output_2 = output_2.squeeze().reshape(1,255)
# LSTM
output_2 = torch.cat((x,output_2),1)
output_2 = output_2.unsqueeze(0)
output_3 , h_state_3_c= self.LSTM_layer_3(output_2,h_state_3_c ) # #,self.hidden_cell
a,b,c = output_3.shape
#
output_4 = F.relu(self.linear4(output_3.view(-1,c)))
value = torch.tanh(self.linear5(output_4))
return value ,(h_state_cv_c[0].data,h_state_cv_c[1].data),(h_state_n_c[0].data,h_state_n_c[1].data),(h_state_3_c[0].data,h_state_3_c[1].data)
def main():
################ load ###################
#train_agent
actor_train_path = os.path.abspath(os.curdir)+'/Generate_Traffic_Flow_MAS_RL/weight/AC_TD3_actor.pkl'
critic_train_path = os.path.abspath(os.curdir)+'/Generate_Traffic_Flow_MAS_RL/weight/AC_TD3_critic.pkl'
if os.path.exists(actor_train_path):
actor_train = Actor(state_size, action_size).to(device)
actor_train.load_state_dict(torch.load(actor_train_path))
print('Actor_Train Model loaded')
else:
actor_train = Actor(state_size, action_size).to(device)
if os.path.exists(critic_train_path):
critic_train = Critic(state_size, action_size).to(device)
critic_train.load_state_dict(torch.load(critic_train_path))
print('Critic_Train Model loaded')
else:
critic_train = Critic(state_size, action_size).to(device)
critic_next_train = Critic(state_size, action_size).to(device)
critic_next_train.load_state_dict(critic_train.state_dict())
#agents
actor_path = os.path.abspath(os.curdir)+'/Generate_Traffic_Flow_MAS_RL/weight/AC_TD_MAS_actor.pkl'
if os.path.exists(actor_path):
actor = Actor(state_size, action_size).to(device)
actor.load_state_dict(torch.load(actor_path))
print('Actor Model loaded')
print("Waiting for GAMA...")
################### initialization ########################
reset()
episode = 0
training_stage = 65
lr = 0.0001
sample_lr = [
0.0001, 0.00009, 0.00008, 0.00007, 0.00006, 0.00005, 0.00004, 0.00003,
0.00002, 0.00001, 0.000009, 0.000008, 0.000007, 0.000006, 0.000005,
0.000004, 0.000003, 0.000002, 0.000001
]
if episode >training_stage : #50 100
try:
lr = sample_lr[int(episode //training_stage)]*0.01
except(IndexError):
lr = 0.000001*0.9#* (0.9 ** ((episode-1000) // 60))
optimizerA = optim.Adam(actor_train.parameters(), lr, betas=(0.95, 0.999))
optimizerC = optim.Adam(critic_train.parameters(), lr, betas=(0.95, 0.999))
values = []
rewards = []
masks = []
total_loss = []
total_rewards = []
loss = []
average_speed = []
value = 0
gama = 0.9
over = 0
log_prob = 0
memory = Memory ()
A_T,state,reward,done,time_pass,over,average_speed_NPC = GAMA_connect( )
print("Connected")
################## start #########################
while over!= 1:
#training_agent
if A_T == 0:
#普通の場合
average_speed.append(state[0])
if(done == 0 and time_pass != 0):
#前回の報酬
reward = torch.tensor([reward], dtype=torch.float, device=device)
rewards.append(reward)
state = torch.DoubleTensor(state).reshape(1,state_size).to(device)
state_img = generate_img_train()
tensor_cv = torch.from_numpy(np.transpose(state_img, (2, 0, 1))).double().to(device)/255
if len(memory.states_next) ==0:
#for _ in range(3):
memory.states_next = memory.states
memory.states_next[2] = state
memory.states_img_next = memory.states_img
memory.states_img_next [2]= tensor_cv
else:
del memory.states_next[:1]
del memory.states_img_next[:1]
memory.states_next.append(state)
memory.states_img_next.append(tensor_cv)
state_next = torch.stack(memory.states_next).to(device).detach()
tensor_cv_next = torch.stack(memory.states_img_next).to(device).detach()
value_next,_,_,_ = critic_next_train(state_next,tensor_cv_next,h_state_cv_c,h_state_n_c,h_state_3_c ) #_next
with torch.autograd.set_detect_anomaly(True):
# TD:r(s) + gama*v(s+1) - v(s)
advantage = reward.detach() + gama*value_next.detach() - value
actor_loss = -(log_prob * advantage.detach())
critic_loss = (reward.detach() + gama*value_next.detach() - value).pow(2)
optimizerA.zero_grad()
optimizerC.zero_grad()
critic_loss.backward()
actor_loss.backward()
loss.append(critic_loss)
optimizerA.step()
optimizerC.step()
critic_next_train.load_state_dict(critic_train.state_dict())
del memory.states[:1]
del memory.states_img[:1]
memory.states.append(state)
memory.states_img.append(tensor_cv)
state = torch.stack(memory.states).to(device).detach()
tensor_cv = torch.stack(memory.states_img).to(device).detach()
value,h_state_cv_c,h_state_n_c,h_state_3_c = critic_train(state,tensor_cv,h_state_cv_c,h_state_n_c,h_state_3_c)
action,log_prob = actor_train(state,tensor_cv)
log_prob = log_prob.unsqueeze(0)
send_to_GAMA([[1,float(action.cpu().numpy()*10)]]) #行
masks.append(torch.tensor([1-done], dtype=torch.float, device=device))
values.append(value)
# 終わり
elif done == 1:
average_speed.append(state[0])
send_to_GAMA([[1,0]])
#先传后计算
print(state)
rewards.append(reward) #contains the last
reward = torch.tensor([reward], dtype=torch.float, device=device)
rewards.append(reward) #contains the last
total_reward = sum(rewards).cpu().detach().numpy()
total_rewards.append(total_reward)
with torch.autograd.set_detect_anomaly(True):
advantage = reward.detach() - value #+ last_value 最后一回的V(s+1) = 0
actor_loss = -( log_prob * advantage.detach())
critic_loss = (reward.detach() - value).pow(2) #+ last_value
optimizerA.zero_grad()
optimizerC.zero_grad()
critic_loss.backward()
actor_loss.backward()
loss.append(critic_loss)
optimizerA.step()
optimizerC.step()
critic_next_train.load_state_dict(critic_train.state_dict())
print("----------------------------------Net_Trained---------------------------------------")
print('--------------------------Iteration:',episode,'over--------------------------------')
episode += 1
values = []
rewards = []
loss_sum = sum(loss).cpu().detach().numpy()
total_loss.append(loss_sum)
cross_loss_curve(loss_sum.squeeze(0),total_reward,save_curve_pic,save_critic_loss,save_reward, np.mean(average_speed),save_speed, average_speed_NPC, save_NPC_speed)
#total_loss,total_rewards#np.mean(average_speed)/10
loss = []
average_speed = []
memory.clear_memory()
torch.save(actor_train.state_dict(),actor_train_path)
torch.save(critic_train.state_dict(),critic_train_path)
if episode >training_stage : #50 100
try:
lr = sample_lr[int(episode //training_stage)]*0.01
except(IndexError):
lr = 0.000001*0.9#* (0.9 ** ((episode-1000) // 60))
optimizerA = optim.Adam(actor_train.parameters(), lr, betas=(0.95, 0.999))
optimizerC = optim.Adam(critic_train.parameters(), lr, betas=(0.95, 0.999))
#最初の時
if time_pass == 0:
print('Iteration:',episode,"lr:",lr)
state = np.reshape(state,(1,len(state)))
state_img = generate_img_train()
tensor_cv = torch.from_numpy(np.transpose(state_img, (2, 0, 1))).double().to(device)/255
state = torch.DoubleTensor(state).reshape(1,state_size).to(device)
for _ in range(3):
memory.states.append(state)
memory.states_img.append(tensor_cv)
state = torch.stack(memory.states).to(device).detach() ###
tensor_cv = torch.stack(memory.states_img).to(device).detach()
value,h_state_cv_c,h_state_n_c,h_state_3_c = critic_train(state,tensor_cv) #dist, # now is a tensoraction = dist.sample()
action,log_prob = actor_train(state,tensor_cv)
print("acceleration: ",action.cpu().numpy())
send_to_GAMA([[1,float(action.cpu().numpy()*10)]])
#agents
if A_T == 1:
state = [torch.DoubleTensor(elem).reshape(1,state_size).to(device) for elem in state]
state = torch.stack(state).to(device).detach()
tensor_cv_MAS = generate_img()
tensor_cv_MAS = [torch.from_numpy(np.transpose(elem, (2, 0, 1))).double().to(device)/255 for elem in tensor_cv_MAS]
tensor_cv_MAS = torch.stack(tensor_cv_MAS).to(device).detach()
action,_ = actor(state,tensor_cv_MAS)
send_to_GAMA([[1,float(action.cpu().numpy()*10)]])
A_T,state,reward,done,time_pass,over,average_speed_NPC = GAMA_connect()
return None
if __name__ == '__main__':
main()