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Temporal Difference Learning Applied to a High-Performance Game-Playing Program

机译:时间差异学习应用于高性能游戏程序

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The temporal difference (TD) learning algorithm offers the hope that the arduous task of manually tuning the evaluation function weights of game-playing programs can be automated. With one exception (TD-Gammon) TD learning has not been demonstrated to be effective in a high-performance, world class game-playing program. Further, there has been doubt expressed by game-program developers that learned weights could compete with the best hand-tuned weights. Chinook is the World Man-Machine Checkers Champion. Its weights were manually tuned over 5 years. This paper shows that TD learning is capable of competing with the best human effort.
机译:时间差异(TD)学习算法提供了手动调整游戏播放程序评估功能权重的艰巨任务的希望可以自动化。凭借一个例外(TD-Gammon)TD学习尚未证明在高性能,世界级游戏节目中有效。此外,曾经有疑问由游戏计划开发人员表达,学习权重可以与最佳的手工调整权重竞争。 Chinook是世界男子机器棋冠军。它的重量超过5年手动调整。本文表明,TD学习能够与最佳人性化竞争。

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