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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.09150 [cs.LG] |
| (or arXiv:2605.09150v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.09150 arXiv-issued DOI via DataCite (pending registration) |
From: Vlad Murgoci [view email]
[v1]
Sat, 9 May 2026 20:26:35 UTC (3,158 KB)
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