Abstract
We present an asymmetric co-evolutionary learning algorithm for imperfect-information zero-sum games. This algorithm is designed so that the fitness of the individual agents is calculated in a way that is compatible with the goal of game-theoretic optimality. This compatibility has been somewhat lacking in previous co-evolutionary approaches, as these have often depended on unwarranted assumptions about the absolute and relative strength of players. Our algorithm design is tested on a game for which the optimal strategy is known, and is seen to work well.
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Halck, O.M., Dahl, F.A. (2000). Asymmetric Co-evolution for Imperfect-Information Zero-Sum Games. In: López de Mántaras, R., Plaza, E. (eds) Machine Learning: ECML 2000. ECML 2000. Lecture Notes in Computer Science(), vol 1810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45164-1_18
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DOI: https://doi.org/10.1007/3-540-45164-1_18
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