Ensemble strategy learning for imperfect information games

Weilin Yuan, Shaofei Chen, Peng Li, Jing Chen

Abstract: Algorithms with several paradigms (such as rule-based methods, game theory and reinforcement learning) have achieved great success in solving imperfect information games (IIGs). However, agents based on a single paradigm tend to be brittle in certain aspects due to the paradigm’s weaknesses. In this paper, we first present three base-solvers with diversified paradigms for IIGs, and then combine them to design three ensemble-solvers (including an attention ensemble-solver, a gradient ensemble-solver and an evolution ensemble-solver) to learn ensemble strategies given base-solvers’ strengths. We evaluate our methods on Leduc poker with nonstationary opponents and limited games. The results show that our ensemble strategy learning method can effectively integrate the advantages of various advanced individual algorithms and significantly outperform them.

External IDs:dblp:journals/ijon/YuanCLC23