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A new technical report on temporal difference (TD) learning for games and "self-play" algorithms for game-agent training is available. This report by Wolfgang Konen features a gentle introduction to TD learning for game play and gives hints for the practioner on the implementation of such algorithms . It shows the references to the most recent applications in this field and discusses in an appendix the more advanced topic of eligibility traces and how and why they work.

This report should be a help for people starting new in the field of TD learning for games and for people who work already in this field but struggle with specific details. It is an updated English translation of an earlier report in German language.


Konen, Wolfgang: Reinforcement Learning for Board Games: The Temporal Difference Algorithm. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Sciences, 2015.

PDF English

PDF German