IJCAI

Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning

将树搜索、生成模型和纳什谈判概念结合在博弈论强化学习中

Zun Li, Marc Lanctot, Kevin R. McKee, Luke Marris, Ian Gemp, Daniel Hennes, Paul Muller, Kate Larson, Yoram Bachrach, Michael P. Wellman

机构 * Google DeepMind（谷歌DeepMind） ； University of Waterloo（滑铁卢大学） ； University of Michigan（密歇根大学） 纠错

AI总结 本文提出基于深度博弈论强化学习的多智能体训练框架，通过生成最佳响应算法GenBR和纳什谈判理论构建对手混合策略，提升在不完全信息域中的对手建模能力。

Comments Accepted by IJCAI'25 main track

Journal ref Proc. 34th Int. Joint Conf. Artif. Intell. (IJCAI 2025), pp. 161-169

Runtime Analyses of NSGA-III on Many-Objective Problems: Provable Exponential Speedup via Stochastic Population Update

NSGA-III在多目标问题上的运行时间分析：通过随机种群更新实现可证明的指数加速

Andre Opris

AI总结 本文通过严格运行时间分析，揭示NSGA-III在多目标问题上的性能，证明随机种群更新机制在多目标多模态问题中可实现指数加速。

Comments This is the long version of the paper with the title "A First Runtime Analysis of NSGA-III on a Many-Objective Multimodal Problem: Provable Exponential Speedup via Stochastic Population Update" already appeared at IJCAI 2025