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AlphaExploitem: Going Beyond the Nash Equilibrium in Poker by Learning to Exploit Suboptimal Play

Vlad Murgoci, Matthijs Spaan, and Yaniv Oren. AlphaExploitem: Going Beyond the Nash Equilibrium in Poker by Learning to Exploit Suboptimal Play. arXiv:2605.09150, 2026.

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Abstract

Poker is an imperfect information game that has served as a long-standing benchmark for decision-making under uncertainty. To maximize utility beyond the Nash equilibrium, an agent can deviate from Nash-equilibrium policies to exploit suboptimal play. We introduce AlphaExploitem, which extends the competitive RL poker agent AlphaHoldem by using a hierarchical transformer encoder that enables reasoning over previously played hands and modifying the training procedure with the inclusion of a diverse pool of exploitable opponents to facilitate learning to exploit. We train and evaluate AlphaExploitem on two standard benchmarks for imperfect-information games. Empirically, AlphaExploitem successfully exploits weak play by both in- and out-of-distribution opponents, without losing performance against NE opponents.

BibTeX Entry

@Misc{Murgoci26arxiv,
  author =       {Vlad Murgoci and Matthijs Spaan and Yaniv Oren},
  title =        {{AlphaExploitem}: Going Beyond the {N}ash
                  Equilibrium in Poker by Learning to Exploit
                  Suboptimal Play},
  howpublished = {arXiv:2605.09150},
  year =         2026
}

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