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A_C_TD_CNN_MAS.py
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A_C_TD_CNN_MAS.py
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from utils import cross_loss_curve, GAMA_connect,reset,send_to_GAMA
from CV_input import generate_img
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")
state_size = 6
action_size = 1
torch.set_default_tensor_type(torch.DoubleTensor)
class Memory:
def __init__(self,h_cv,h_n):
self.h_state_cv_a = h_cv
self.h_state_n_a = h_n
def set_hidden(self,h_cv,h_n):
self.h_state_cv_a = h_cv
self.h_state_n_a = h_n
""" # LSTM
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 = nn.Linear(13456, 256) # *3
self.lstm_CNN = nn.LSTM(256,85,batch_first=True)
#
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,batch_first=True)
#self.LSTM_layer_3 = nn.LSTM(511,128,1, batch_first=True)
self.linear3 = nn.Linear(510,128)
self.linear4 = nn.Linear(128,32)
self.mu = nn.Linear(32,self.action_size) #256 linear2
self.sigma = nn.Linear(32,self.action_size)
def forward(self, state,tensor_cv,h_state_cv_a=(torch.zeros(1,1,85).to(device),
torch.zeros(1,1,85).to(device)),h_state_n_a=(torch.zeros(1,3,85).to(device),
torch.zeros(1,3,85).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) #[3, 16, 29, 29]
x = F.relu(self.linear_CNN(x))#.reshape(3,1,256)
x,h_state_cv = self.lstm_CNN(x.unsqueeze(0),h_state_cv_a) #.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_a = self.lstm3(output_2,h_state_n_a)
output_2 = F.relu(output_2) .squeeze().reshape(1,255)
# LSTM
output_2 = torch.cat((x,output_2),1)
output_3 = F.relu(self.linear3(output_2))
#
output_4 = 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)
return action,(h_state_cv_a[0].data,h_state_cv_a[1].data),(h_state_n_a[0].data,h_state_n_a[1].data)
"""
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,entropy
def main():
################ load ###################
actor_path = os.path.abspath(os.curdir)+'/Generate_Traffic_Flow_MAS_RL/weight/AC_TD2_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')
else:
actor = Actor(state_size, action_size).to(device)
print("Waiting for GAMA...")
################### initialization ########################
reset()
Using_LSTM = False
test = "GAMA"
N_agent = 20
list_hidden = []
count = 0
################## start #########################
state = GAMA_connect(test)
print("Connected")
while True:
if Using_LSTM == False:
state = [torch.DoubleTensor(elem).reshape(1,state_size).to(device) for elem in state]
state = torch.stack(state).to(device).detach()
tensor_cv = generate_img()
tensor_cv = [torch.from_numpy(np.transpose(elem, (2, 0, 1))).double().to(device)/255 for elem in tensor_cv]
tensor_cv = torch.stack(tensor_cv).to(device).detach()
action,h_state_cv_a,h_state_n_a = actor(state,tensor_cv)
send_to_GAMA([[1,float(action.cpu().numpy()*10)]])
else:
if len(list_hidden) < N_agent:
state = [torch.DoubleTensor(elem).reshape(1,state_size).to(device) for elem in state]
state = torch.stack(state).to(device).detach()
tensor_cv = generate_img()
tensor_cv = [torch.from_numpy(np.transpose(elem, (2, 0, 1))).double().to(device)/255 for elem in tensor_cv]
tensor_cv = torch.stack(tensor_cv).to(device).detach()
action,h_state_cv_a,h_state_n_a = actor(state,tensor_cv)
send_to_GAMA([[1,float(action.cpu().numpy()*10)]])
list_hidden.append(Memory(h_state_cv_a,h_state_n_a))
count += 1
else:
state = [torch.DoubleTensor(elem).reshape(1,state_size).to(device) for elem in state]
state = torch.stack(state).to(device).detach()
tensor_cv = generate_img()
tensor_cv = [torch.from_numpy(np.transpose(elem, (2, 0, 1))).double().to(device)/255 for elem in tensor_cv]
tensor_cv = torch.stack(tensor_cv).to(device).detach()
action,h_state_cv_a,h_state_n_a = actor(state,tensor_cv,
list_hidden[count%N_agent].h_state_cv_a,list_hidden[count%N_agent].h_state_n_a)
send_to_GAMA([[1,float(action.cpu().numpy()*10)]])
list_hidden[count%N_agent].set_hidden(h_state_cv_a,h_state_n_a)
count += 1
state = GAMA_connect(test)
return None
if __name__ == '__main__':
main()