Evolving Nash-optimal poker strategies using evolutionary computation

This paper focuses on the development of a competitive computer player for the one versus one Texas Hold'em poker using evolutionary algorithms (EA). A Texas Hold'em game engine is first constructed where an efficient odds calculator is programmed to allow for the abstraction of a player's cards, which yield important but complex information. Effort is directed to realize an optimal player that will play close to the Nash equilibrium (NE) by proposing a new fitness criterion. Preliminary studies on a simplified version of poker highlighted the intransitivity nature of poker. The evolved player displays strategies that are logical but reveals insights that are hard to comprehend e.g., bluffing. The player is then benchmarked against Poki and PSOpti, which is the best heads-up Texas Hold'em artificial intelligence to date and plays closest to the optimal Nash equilibrium. Despite the much constrained chromosomal strategy representation, simulated results verified that evolutionary algorithms are effective in creating strategies that are comparable to Poki and PSOpti in the absence of expert knowledge.