Self-Play Reinforcement Learning under Imperfect Information in Big 2 事件

Self-Play Reinforcement Learning under Imperfect Information in Big 2 arXiv:2605.28863v1 Announce Type: cross Abstract: Imperfect-information multiplayer games test whether agents can act under hidden information, sparse rewards, and non-stationary opponents. We study these challenges in Big 2, a four-player imperfect-information card game. We develop a self-play RL framework for Big 2 that enables controlled comparisons between policy-gradient and value-approximating agents. Under a common env