HEX与神经动力规划
HEX and Neurodynamic Programming
摘要：
十六进制是一个复杂的游戏，具有高分支因子。这是第一次尝试在不使用博弈树结构和相关修剪方法的情况下解决十六进制问题。我们也放弃任何关于虚拟连接或半虚拟连接的启发式信息，这些信息以前曾在所有已知的计算机版本的游戏中使用过。H-搜索算法是找到这种连接的基础，并已成功地用于以前的十六进制播放代理已被放弃。相反，我们使用的是强化学习，通过自我发挥和近似通过神经网络绕过高分支因子和维护大型表的问题，状态动作评估。我们的代码主要基于NeuroHex。灵感来自AlphaGo Zero最近的成功。
英文摘要：
Hex is a complex game with a high branching factor. For the first time Hex is being attempted to be solved without the use of game tree structures and associated methods of pruning. We also are abstaining from any heuristic information about Virtual Connections or Semi Virtual Connections which were previously used in all previous known computer versions of the game. The H-search algorithm which was the basis of finding such connections and had been used with success in previous Hex playing agents has been forgone. Instead what we use is reinforcement learning through self play and approximations through neural networks to by pass the problem of high branching factor and maintaining large tables for state-action evaluations. Our code is based primarily on NeuroHex. The inspiration is drawn from the recent success of AlphaGo Zero.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
