Multi-Criteria Comparison of Coevolution and Temporal Difference Learning on Othello

摘要

We compare Temporal Difference Learning (TDL) with Coevolutionary Learning (CEL) on Othello. Apart from using three popular single-criteria performance measures: (ⅰ) generalization performance or expected utility, (ⅱ) average results against a hand-crafted heuristic and (ⅲ) result in a head to head match, we compare the algorithms using performance profiles. This multi-criteria performance measure characterizes player's performance in the context of opponents of various strength. The multi-criteria analysis reveals that although the generalization performance of players produced by the two algorithms is similar, TDL is much better at playing against strong opponents, while CEL copes better against weak ones. We also find out that the TDL produces less diverse strategies than CEL. Our results confirms the usefulness of performance profiles as a tool for comparison of learning algorithms for games.