蒙特卡罗树搜索+深度学习 -- AlphaGo原版论文阅读笔记 目录(?)[+] 原版论文是<Mastering the game of Go with deep neural networks and tree search>,有时间的还是建议读一读,没时间的可以看看我这篇笔记凑活一下.网上有一些分析AlphaGo的文章,但最经典的肯定还是原文,还是踏踏实实搞懂AlphaGo的基本原理我们再来吹牛逼吧. 需要的一些背景 对围棋不了解的,其实也不怎么影响,因为只有feature e…
转载请声明 http://blog.csdn.net/u013390476/article/details/50925347 前言: 围棋的英文是 the game of Go,标题翻译为:<用深度神经网络和树搜索征服围棋>.译者简单介绍:大三,211,计算机科学与技术专业,平均分92分,专业第一.为了更好地翻译此文.译者查看了非常多资料.译者翻译此论文已尽全力,不足之处希望读者指出. 在AlphaGo的影响之下,全社会对人工智能的关注进一步提升. 3月12日,AlphaGo 第三次击败李世石…
//树形菜单搜索方法 function searchTree(treeObj,parentNode,searchCon){ var children; for(var i=0;i<parentNode.length;i++){ //循环顶级 node children = $(treeObj).tree('getChildren',parentNode[i].target);//获取顶级node下所有子节点 if(ch…
题目信息 1079. Total Sales of Supply Chain (25) 时间限制250 ms 内存限制65536 kB 代码长度限制16000 B A supply chain is a network of retailers(零售商), distributors(经销商), and suppliers(供应商)– everyone involved in moving a product from supplier to customer. Starting from one…
题目信息 1076. Forwards on Weibo (30) 时间限制3000 ms 内存限制65536 kB 代码长度限制16000 B Weibo is known as the Chinese version of Twitter. One user on Weibo may have many followers, and may follow many other users as well. Hence a social network is formed with follo…
Mastering the game of Go with deep neural networks and tree search Nature 2015 这是本人论文笔记系列第二篇 Nature 的文章了,第一篇是 DQN.好紧张!好兴奋! 本文可谓是在世界上赚够了吸引力! 围棋游戏被看做是 AI 领域最有挑战的经典游戏,由于其无穷的搜索空间 和 评价位置和移动的困难.本文提出了一种新的方法给计算机来玩围棋游戏,即:利用 "value network" 来评价广泛的位置 和 “p…
前言: 本文是根据的文章Introduction to Monte Carlo Tree Search by Jeff Bradberry所写. Jeff Bradberry还提供了一整套的例子,用python写的. board game server board game client Tic Tac Toe board AI implementation of Tic Tac Toe 阿袁工作的第一天 - 蒙特卡罗树搜索算法 - 游戏的通用接口board 和 player 阿袁看到阿静最近在…
MIT(Deep Learning for Self-Driving Cars) CMU(Deep Reinforcement Learning and Control ) 参考网址: 1 Deep Learning for Self-Driving Cars -- 6.S094 http://selfdrivingcars.mit.edu/ 2 Deep Reinforcement Learning and Control -- 10703 https://katefvision.gi…
Reinforcement Learning: An Introduction (second edition) - Chapter 1,2 Chapter 1 1.1 Self-Play Suppose, instead of playing against a random opponent, the reinforcement learning algorithm described above played against itself, with both sides learning…
