目录
1.达到的目的
2.思路
2.1.强化学习（RL Reinforcement Learing）
2.2.深度学习（卷积神经网络CNN）
3.踩过的坑
4.代码实现（python3.5）
5.运行结果与分析
#!/usr/bin/env python
from __future__ import print_function
import tensorflow as tf
import cv2
import sys
sys.path.append("game/")
try:
from . import wrapped_flappy_bird as game
except Exception:
import wrapped_flappy_bird as game
import random
import numpy as np
from collections import deque
'''
先观察一段时间（OBSERVE = 1000 不能过大），
获取state(连续的4帧) => 进入训练阶段（无上限）=> action
'''
GAME = 'bird' # the name of the game beingplayed for log files
ACTIONS = 2 # number of valid actions 往上 往下
GAMMA = 0.99 # decay rate of pastobservations
OBSERVE = 1000. # timesteps to observebefore training
EXPLORE = 3000000. # frames over which toanneal epsilon
FINAL_EPSILON = 0.0001 # final value ofepsilon 探索
INITIAL_EPSILON = 0.1 # starting value ofepsilon
REPLAY_MEMORY = 50000 # number of previoustransitions to remember
BATCH = 32 # size of minibatch
FRAME_PER_ACTION = 1
# GAME = 'bird' # the name of the gamebeing played for log files
# ACTIONS = 2 # number of valid actions
# GAMMA = 0.99 # decay rate of pastobservations
# OBSERVE = 100000. # timesteps to observebefore training
# EXPLORE = 2000000. # frames over which toanneal epsilon
# FINAL_EPSILON = 0.0001 # final value ofepsilon
# INITIAL_EPSILON = 0.0001 # starting valueof epsilon
# REPLAY_MEMORY = 50000 # number ofprevious transitions to remember
# BATCH = 32 # size of minibatch
# FRAME_PER_ACTION = 1
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev = 0.01)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.01, shape = shape)
return tf.Variable(initial)
# padding = ‘SAME’=> new_height = new_width = W / S （结果向上取整）
# padding = ‘VALID’=> new_height = new_width = (W – F 1) / S （结果向上取整）
def conv2d(x, W, stride):
return tf.nn.conv2d(x, W, strides = [1, stride, stride, 1], padding ="SAME")
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1],padding = "SAME")
"""
数据流：80 * 80 * 4
conv1(8 * 8 * 4 * 32, Stride = 4) pool(Stride = 2)-> 10 * 10 * 32(height = width = 80/4 = 20/2 = 10)
conv2(4 * 4 * 32 * 64, Stride = 2) -> 5 * 5* 64 pool(Stride = 2)-> 3 * 3 * 64
conv3(3 * 3 * 64 * 64, Stride = 1) -> 3 * 3* 64 = 576
576 在定义h_conv3_flat变量大小时需要用到，以便进行FC全连接操作
"""
def createNetwork():
#network weights
W_conv1 = weight_variable([8, 8, 4, 32])
b_conv1 = bias_variable([32])
W_conv2 = weight_variable([4, 4, 32, 64])
b_conv2 = bias_variable([64])
W_conv3 = weight_variable([3, 3, 64, 64])
b_conv3 = bias_variable([64])
W_fc1 = weight_variable([576, 512])
b_fc1 = bias_variable([512])
#W_fc1 = weight_variable([1600, 512])
#b_fc1 = bias_variable([512])
W_fc2 = weight_variable([512, ACTIONS])
b_fc2 = bias_variable([ACTIONS])
#input layer
s= tf.placeholder("float", [None, 80, 80, 4])
#hidden layers
h_conv1 = tf.nn.relu(conv2d(s, W_conv1, 4) b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2, 2) b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv3, 1) b_conv3)
h_pool3 = max_pool_2x2(h_conv3)
h_pool3_flat = tf.reshape(h_pool3, [-1, 576])
#h_conv3_flat = tf.reshape(h_conv3, [-1, 1600])
h_fc1 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc1) b_fc1)
#h_fc1 = tf.nn.relu(tf.matmul(h_conv3_flat, W_fc1) b_fc1)
#readout layer
readout= tf.matmul(h_fc1, W_fc2) b_fc2
return s, readout, h_fc1
def trainNetwork(s, readout, h_fc1, sess):
#define the cost function
a= tf.placeholder("float", [None, ACTIONS])
y= tf.placeholder("float", [None])
#reduction_indices = axis 0 : 列 1: 行
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