Finding Robust Texas Hold’em Poker Strategies

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Finding Robust Texas Hold’em Poker Strategies

Coevolutionary algorithms hold great promise for the automatic discovery of strong strategies in games, but may run into problems with intransitive superiority relationships.

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Finding Robust Texas Hold’em Poker Strategies

Coevolutionary algorithms hold great promise for the automatic discovery of strong strategies in games, but may run into problems with intransitive superiority relationships. Pareto coevolution gets around this diculty by explicitly treating performance against each player in the population as a dimension for multi-objective optimization. Pareto coevolution was previously applied [1] to Texas Hold’em poker, a complex game of imperfect information. The current paper describes work on improving the poker strategy representation and on improving the Pareto selection algorithm by incorporating deterministic crowding, a diversity maintenance technique. Reasonably strong poker strategies are evolved, but only when evolution commences from a hand-coded starting point.

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