Mastering Delayed Feedback: WOFTRL Unlocks Optimal AI Learning in Games
By Yuma Fujimoto, Kenshi Abe, Kaito Ariu
Published on November 10, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.LG updates on arXiv.org.
Summary
This study tackles the critical issue of time-delayed feedback in multi-agent learning within game environments, where agents independently optimize strategies. It demonstrates that even a single-step delay significantly degrades the performance of existing algorithms like Optimistic Follow-the-Regularized-Leader (OFTRL) in terms of social regret and convergence. To counteract this, the paper proposes Weighted OFTRL (WOFTRL), which applies an n-times weight to the prediction vector of future rewards. Theoretically, WOFTRL proves to recover OFTRL's strong performance-achieving constant social regret in general-sum normal-form games and last-iterate convergence to the Nash equilibrium in poly-matrix zero-sum games-provided the optimistic weight surpasses the time delay, findings further supported by experimental results.
Why It Matters
This research is profoundly significant for professionals in the AI space, particularly those working with multi-agent systems and real-world applications where information flow is rarely instantaneous. "Learning in games" serves as a crucial abstraction for numerous complex scenarios beyond board games, encompassing competitive markets, autonomous vehicle coordination, distributed resource management, and even adversarial AI interactions. The finding that even minor delays severely hamper state-of-the-art algorithms like OFTRL highlights a fundamental fragility in current multi-agent learning paradigms when faced with realistic latency. WOFTRL's ability to robustly overcome these delays, ensuring stable convergence and optimal outcomes (like Nash equilibrium), represents a substantial leap forward. It implies that AI systems can now learn and adapt more effectively in environments characterized by asynchronous feedback, noisy communication, or inherent system lags. This directly translates to more reliable, efficient, and intelligent autonomous agents capable of operating in dynamic, imperfect real-world settings, accelerating progress in areas from complex supply chain optimization to advanced robotics and defense systems, where timely and accurate decision-making under delayed information is paramount. This work underscores an ongoing trend in AI research to move beyond idealized environments and tackle the messier, yet more impactful, challenges of real-world deployment.