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main.py
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main.py
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import pdb
import cv2
import sys
import os
#sys.path.append("game/")
from game import wrapped_flappy_bird as game
import random
import numpy as np
from collections import deque
import torch
from torch.autograd import Variable
import torch.nn as nn
GAME = 'bird' # the name of the game being played for log files
ACTIONS = 2 # number of valid actions
GAMMA = 0.99 # decay rate of past observations
OBSERVE = 1000. # timesteps to observe before training
EXPLORE = 2000000. # frames over which to anneal epsilon
FINAL_EPSILON = 0.0001 # final value of epsilon
INITIAL_EPSILON = 0.0001 # starting value of epsilon
REPLAY_MEMORY = 50000 # number of previous transitions to remember
BATCH_SIZE = 32 # size of minibatch
FRAME_PER_ACTION = 1
UPDATE_TIME = 100
width = 80
height = 80
def preprocess(observation):
observation = cv2.cvtColor(cv2.resize(observation, (80, 80)), cv2.COLOR_BGR2GRAY)
ret, observation = cv2.threshold(observation,1,255,cv2.THRESH_BINARY)
return np.reshape(observation, (1,80,80))
class DeepNetWork(nn.Module):
def __init__(self,):
super(DeepNetWork,self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=4, out_channels=32, kernel_size=8, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2)
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2, padding=1),
nn.ReLU(inplace=True)
)
self.conv3 = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True)
)
self.fc1 = nn.Sequential(
nn.Linear(1600,256),
nn.ReLU()
)
self.out = nn.Linear(256,2)
def forward(self, x):
x = self.conv1(x); x = self.conv2(x);
x = self.conv3(x); x = x.view(x.size(0),-1)
x = self.fc1(x); return self.out(x)
class BrainDQNMain(object):
def save(self):
print("save model param")
torch.save(self.Q_net.state_dict(), 'params3.pth')
def load(self):
if os.path.exists("params3.pth"):
print("load model param")
self.Q_net.load_state_dict(torch.load('params3.pth'))
self.Q_netT.load_state_dict(torch.load('params3.pth'))
def __init__(self,actions):
self.replayMemory = deque() # init some parameters deque返回一个类似双向链表的数据结构，即可以从两端操作的数据结构
self.timeStep = 0
self.epsilon = INITIAL_EPSILON
self.actions = actions
self.Q_net=DeepNetWork()
self.Q_netT=DeepNetWork();
self.load()
self.loss_func=nn.MSELoss()
LR=1e-6
self.optimizer = torch.optim.Adam(self.Q_net.parameters(), lr=LR)
def train(self): # Step 1: obtain random minibatch from replay memory
minibatch = random.sample(self.replayMemory, BATCH_SIZE)
state_batch = [data[0] for data in minibatch] #32*4*80*80
action_batch = [data[1] for data in minibatch] #32*2
reward_batch = [data[2] for data in minibatch] #32*1
nextState_batch = [data[3] for data in minibatch] # Step 2: calculate y
#print("action_batch", action_batch)
#print("reward_batch",reward_batch)
#print("nextState_batch",nextState_batch)
y_batch = np.zeros([BATCH_SIZE,1])
nextState_batch=np.array(nextState_batch) #print("train next state shape")
#print(nextState_batch.shape)
nextState_batch=torch.Tensor(nextState_batch)
action_batch=np.array(action_batch)
index=action_batch.argmax(axis=1) #32*1 即一维数组，共32个元素
#print("action "+str(index))
index=np.reshape(index,[BATCH_SIZE,1])
action_batch_tensor=torch.LongTensor(index)
QValue_batch = self.Q_netT(nextState_batch)
QValue_batch=QValue_batch.detach().numpy()
print("Qvalue_batch: ", QValue_batch)
print("reward: ",reward_batch)
for i in range(0, BATCH_SIZE):
terminal = minibatch[i][4]
if terminal:
y_batch[i][0]=reward_batch[i]
else:
# 这里的QValue_batch[i]为数组，大小为所有动作集合大小，QValue_batch[i],代表
# 做所有动作的Q值数组，y计算为如果游戏停止，y=rewaerd[i],如果没停止，则y=reward[i]+gamma*np.max(Qvalue[i])
# 代表当前y值为当前reward+未来预期最大值*gamma(gamma:经验系数)
y_batch[i][0]=reward_batch[i] + GAMMA * np.max(QValue_batch[i])
y_batch=np.array(y_batch)
y_batch=np.reshape(y_batch,[BATCH_SIZE,1])
state_batch_tensor=Variable(torch.Tensor(state_batch))
y_batch_tensor=Variable(torch.Tensor(y_batch))
y_predict=self.Q_net(state_batch_tensor).gather(1,action_batch_tensor)
loss=self.loss_func(y_predict,y_batch_tensor)
print("loss is "+str(loss))
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if self.timeStep % UPDATE_TIME == 0:
self.Q_netT.load_state_dict(self.Q_net.state_dict())
self.save()
def setPerception(self,nextObservation,action,reward,terminal): #print(nextObservation.shape)
newState = np.append(self.currentState[1:,:,:],nextObservation,axis = 0) # newState = np.append(nextObservation,self.currentState[:,:,1:],axis = 2)
self.replayMemory.append((self.currentState,action,reward,newState,terminal))
if len(self.replayMemory) > REPLAY_MEMORY:
self.replayMemory.popleft()
if self.timeStep > OBSERVE: # Train the network
self.train()
# print info
state = ""
if self.timeStep <= OBSERVE:
state = "observe"
elif self.timeStep > OBSERVE and self.timeStep <= OBSERVE + EXPLORE:
state = "explore"
else:
state = "train"
#print ("TIMESTEP", self.timeStep, "/ STATE", state, "/ EPSILON", self.epsilon)
self.currentState = newState
self.timeStep += 1
def getAction(self):
currentState = torch.Tensor([self.currentState])
QValue = self.Q_net(currentState)[0]
action = np.zeros(self.actions)
if self.timeStep % FRAME_PER_ACTION == 0:
if random.random() <= self.epsilon:
action_index = random.randrange(self.actions)
#print("choose random action " + str(action_index))
action[action_index] = 1
else:
action_index = np.argmax(QValue.detach().numpy())
#print("choose qnet value action " + str(action_index))
action[action_index] = 1
else:
action[0] = 1 # do nothing
# change episilon
if self.epsilon > FINAL_EPSILON and self.timeStep > OBSERVE:
self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / EXPLORE
return action
def setInitState(self, observation):
self.currentState = np.stack((observation, observation, observation, observation),axis=0)
if __name__ == '__main__':
# Step 1: init BrainDQN
actions = 2
brain = BrainDQNMain(actions) # Step 2: init Flappy Bird Game
flappyBird = game.GameState() # Step 3: play game
# Step 3.1: obtain init state
action0 = np.array([1,0]) # do nothing
observation0, reward0, terminal = flappyBird.frame_step(action0)
# print("observation0",observation0)
# print(observation0.shape)
observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)), cv2.COLOR_BGR2GRAY)
ret, observation0 = cv2.threshold(observation0,1,255,cv2.THRESH_BINARY)
#print("observation0 ",observation0) #这是二维数组，因为已经没有了颜色，每个像素点都只有单通道值
brain.setInitState(observation0) #四个二维数组放在一起拼成一个三维数组，该三维数组就是brain的currentState
# input=[]
# input.append(brain.currentState)
# input=np.array(input)
# print(input.shape)
# print(input)
# out=brain.Q_net(torch.Tensor(input))
# print("out : ",out)
while 1!= 0:
action = brain.getAction() #类似于[1.,0.]的一维数组
#print("action ",action)
nextObservation,reward,terminal = flappyBird.frame_step(action)
#print("reward: ",reward)
nextObservation = preprocess(nextObservation)
#print(nextObservation)
brain.setPerception(nextObservation,action,reward,terminal)