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Excution.py
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Excution.py
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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
#AC_TD_MAS_actor
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 = 9
action_size = 1
Memory_size = 4
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(3,16, kernel_size=3, stride=2, padding=0) # 237*237*N ->57*57*16
self.maxp1 = nn.MaxPool2d(3, stride = 2, padding=0) #79*79*16-> 39*39*32
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=0) # 39*39*16-> 19*19*32
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=0) # 19*19*32 -> 9*9*64
#self.maxp2 = nn.MaxPool2d(3, stride = 2, padding=0) # 19*19*64 -> 9*9*64
self.linear_CNN_1 = nn.Linear(5184, 256)
self.linear_CNN_2 = nn.Linear(256*Memory_size,16*Memory_size) #(768,256)
#
self.state_size = state_size
self.action_size = action_size
self.linear0 = nn.Linear(self.state_size, 64)
self.linear1 = nn.Linear(64, 128)
self.linear2 = nn.Linear(128, 16)#128, 85)
self.linear3 = nn.Linear(32*Memory_size,12)#(511,128)
self.linear4 = nn.Linear(12,8) #(128,32)
self.mu = nn.Linear(8,self.action_size) #32
self.sigma = nn.Linear(8,self.action_size)
self.distribution = torch.distributions.Normal
def forward(self, state,tensor_cv):
# CV
x = F.relu(self.maxp1(self.conv1(tensor_cv)))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
#x = F.relu(self.conv3(x))
x = x.view(x.size(0), -1) #展開
x = F.relu(self.linear_CNN_1(x)).reshape(1,256*Memory_size)
x = F.relu(self.linear_CNN_2(x)).reshape(1,16*Memory_size) #.reshape(1,256)
# num
output_0 = F.relu(self.linear0(state))
output_1 = F.relu(self.linear1(output_0))
output_2 = F.relu(self.linear2(output_1)).reshape(1,16*Memory_size) #(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)
dist = self.distribution(mu, sigma)#MultivariateNormal(mu,sigma) #N(μ,σ^2)
action = dist.sample()
action_logprob = dist.log_prob(action)
action = torch.clamp(action.detach(), -0.8, 0.6)
#entropy = torch.sum(dist.entropy())
#entropy = dist.entropy().mean() #torch.sum(m_probs.entropy())
#entropy = 0.5 + 0.5 * math.log(2 * math.pi) + torch.log(dist.scale)
entropy = -torch.exp(action_logprob) * action_logprob
return action,action_logprob#,entropy
class Critic(nn.Module):
def __init__(self, state_size, action_size):
super(Critic, self).__init__()
self.conv1 = nn.Conv2d(3,16, kernel_size=3, stride=2, padding=0) # 237*237*N ->57*57*16
self.maxp1 = nn.MaxPool2d(3, stride = 2, padding=0) #79*79*16-> 39*39*32
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=0) # 39*39*16-> 19*19*32
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=0) # 19*19*32 -> 9*9*64
#self.maxp2 = nn.MaxPool2d(3, stride = 2, padding=0) # 19*19*64 -> 9*9*64
self.linear_CNN = nn.Linear(5184, 256)
self.lstm_CNN = nn.LSTM(256,16, 1,batch_first=True)
#
self.state_size = state_size
self.action_size = action_size
self.linear0 = nn.Linear(self.state_size, 64)
self.linear1 = nn.Linear(64, 128)
self.linear2 = nn.Linear(128, 128)
self.lstm3 = nn.LSTM(128,16,1, batch_first=True)#85
#
self.LSTM_layer_3 = nn.LSTM(32*Memory_size,16,1, batch_first=True) #510,128
self.linear4 = nn.Linear(16,4) #128,32
self.linear5 = nn.Linear(4, action_size) #32
def forward(self, state, tensor_cv,h_state_cv_c=(torch.zeros(1,Memory_size,16).to(device),
torch.zeros(1,Memory_size,16).to(device)),h_state_n_c=(torch.zeros(1,Memory_size,16).to(device), #1,3,85)
torch.zeros(1,Memory_size,16).to(device)),h_state_3_c=(torch.zeros(1,1,16).to(device),
torch.zeros(1,1,16).to(device))): #(1,1,128)
# CV
x = F.relu(self.maxp1(self.conv1(tensor_cv)))
x = F.relu( self.conv2(x))
x = F.relu(self.conv3(x)).reshape(Memory_size,1,5184)
#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(1,16*Memory_size) #.reshape(1,255) torch.tanh
# num
output_0 = F.relu(self.linear0(state))
output_1 = F.relu(self.linear1(output_0))
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(1,16*Memory_size)
# 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#191
training_stage = 65
lr = 0.0001*0.0
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
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[Memory_size-1] = state
memory.states_img_next = memory.states_img
memory.states_img_next [Memory_size-1]= 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())"""
values = []
rewards = []
loss_sum = 0#sum(loss).cpu().detach().numpy()
total_loss.append(loss_sum)
#loss_sum.squeeze(0)
cross_loss_curve(loss_sum,total_reward,save_curve_pic,save_critic_loss,save_reward, np.mean(average_speed)*10,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)]*0.0
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))
print("----------------------------------Net_Trained---------------------------------------")
print('--------------------------Iteration:',episode,'over--------------------------------')
episode += 1
#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(Memory_size):
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)]])
#NPC 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()