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An Approach to the Development of a Game Agent Based on SOM and Reinforcement Learning

机译：基于SOM和强化学习的游戏代理开发的一种方法

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Recently, the researches that create agents which play board games have been studied actively. According to those studies, those agents have abilities that are comparable to the strongest experts. However, it can be said that those agents depend on the computational capability because that abilities of those agents are realized by thousands of lookahead search. On theotherhand, humanbeingshavenoadvantagescomparedwith numerical capability of computers, however, experts sometimes defeat those agents. In contrast to other approaches, our purpose is to create the agent which requires only low computational capability but is strong, like human beings. To realize our aim, we have proposed to develop the agent based on Self-Organizing Maps and reinforcement learning. From the experimental results, the agent learned by MC-learning achieved a 58% winning rate against the adversary program, so that we have succeeded in improving the winning rate over 10%.
机译：最近，积极研究了播放棋盘游戏的代理商的研究。根据这些研究，这些代理商具有与最强专家相当的能力。然而，可以说这些代理依赖于计算能力，因为这些代理的能力是通过数千个寻求搜索来实现的。在TheOlerhand，人类汉语纳湾VantagesComparedwith电脑的数值能力，然而，专家有时会击败这些代理商。与其他方法相比，我们的目的是创建只需要低计算能力但是强大的代理，如人类。为了实现我们的目标，我们建议根据自组织地图和强化学习开发代理人。从实验结果来看，MC-Learning了解的代理人凭借对手计划取得了58％的胜利率，因此我们已经成功地提高了10％以上的获胜率。

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