Researchers have introduced GARIP, a novel method for improving self-play in two-player zero-sum games. Unlike previous approaches that use fixed or periodically updated references, GARIP utilizes a running average of past policies. This method is theoretically shown to minimize the peak lag of the reference, leading to more stable convergence. Experiments on various games, including matrix games and board games like Connect Four and Othello, demonstrate that GARIP performs comparably to or better than existing methods, particularly in robustness and default hyperparameter settings. AI
IMPACT This research could lead to more efficient training of AI agents in competitive environments.
RANK_REASON Academic paper detailing a new method for game theory and AI. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.MA (Multiagent) →
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