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R. Coulom, “Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search,” Proceedings of the fifth International Conference on Computers and Games, Turin, Italy, 2006, pp. 72-83.

has been cited by the following article:

• TITLE: Self-Play and Using an Expert to Learn to Play Backgammon with Temporal Difference Learning

  AUTHORS: Marco A. Wiering

  KEYWORDS: Board Games, Reinforcement Learning, TD(λ), Self-Play, Learning From Demonstration

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.2 No.2, June 1, 2010

  ABSTRACT: A promising approach to learn to play board games is to use reinforcement learning algorithms that can learn a game position evaluation function. In this paper we examine and compare three different methods for generating training games: 1) Learning by self-play, 2) Learning by playing against an expert program, and 3) Learning from viewing ex-perts play against each other. Although the third possibility generates high-quality games from the start compared to initial random games generated by self-play, the drawback is that the learning program is never allowed to test moves which it prefers. Since our expert program uses a similar evaluation function as the learning program, we also examine whether it is helpful to learn directly from the board evaluations given by the expert. We compared these methods using temporal difference methods with neural networks to learn the game of backgammon.