RVσ(t): A unifying approach to performance and convergence in online multiagent learning

Bikramjit Banerjee, Jing Peng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We present a new multiagent learning algorithm (RVσ(t)) that can guarantee both no-regret performance (all games) and policy convergence (some games of arbitrary size). Unlike its predecessor ReDVaLeR, it (1) does not need to distinguish whether its opponents are self-play or otherwise non-stationary, (2) is allowed to know its portion of any equilibrium that, we argue, leads to convergence in some games in addition to no-regret. Although the regret of RVσ(t) is analyzed in continuous time, we show that it grows slower than in other no-regret techniques like GIGA and GIGA-WoLF. We show that RVσ(t) can converge to coordinated behavior in coordination games, while GIGA, GIGA-WoLF may converge to poorly coordinated (mixed) behaviors.

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Keywords

  • Game theory
  • Multiagent learning

Cite this

Banerjee, B., & Peng, J. (2006). RVσ(t): A unifying approach to performance and convergence in online multiagent learning. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (Vol. 2006, pp. 798-800) https://doi.org/10.1145/1160633.1160775