Multiagent Online Learning in Time-Varying Games
研究了多智能体在随时间演化的博弈中采用镜像下降策略的长期行为，证明在长期稳定的严格单调博弈中策略收敛到纳什均衡，且渐近跟踪演化均衡。
We examine the long-run behavior of multiagent online learning in games that evolve over time. Specifically, we focus on a wide class of policies based on mirror descent, and we show that the induced sequence of play (a) converges to a Nash equilibrium in time-varying games that stabilize in the long run to a strictly monotone limit, and (b) it stays asymptotically close to the evolving equilibrium of the sequence of stage games (assuming they are strongly monotone). Our results apply to both gradient- and payoff-based feedback—that is, when players only get to observe the payoffs of their chosen actions. Funding: This research was partially supported by the European Cooperation in Science and Technology COST Action [Grant CA16228] “European Network for Game Theory” (GAMENET). P. Mertikopoulos is grateful for financial support by the French National Research Agency (ANR) in the framework of the “Investissements d’avenir” program [Grant ANR-15-IDEX-02], the LabEx PERSYVAL [Grant ANR-11-LABX-0025-01], MIAI@Grenoble Alpes [Grant ANR-19-P3IA-0003], and the ALIAS [Grant ANR-19-CE48-0018-01].