Delay and cooperation in nonstochastic bandits

Nicolo Cesa-Bianchi, Claudio Gentile, Yishay Mansour

Research output: Contribution to journalArticlepeer-review

Abstract

We study networks of communicating learning agents that cooperate to solve a common nonstochastic bandit problem. Agents use an underlying communication network to get messages about actions selected by other agents, and drop messages that took more than d hops to arrive, where d is a delay parameter. We introduce Exp3-Coop, a cooperative version of the Exp3 algorithm and prove that with K actions and N agents the average per-agent regret after T rounds is at most of order qd + 1 + KN a=d (T ln K), where a=d is the independence number of the d-th power of the communication graph G. We then show that for any connected graph, for d = pK the regret bound is K1=4pT, strictly better than the minimax regret pKT for noncooperating agents. More informed choices of d lead to bounds which are arbitrarily close to the full information minimax regret pT ln K when G is dense. When G has sparse components, we show that a variant of Exp3-Coop, allowing agents to choose their parameters according to their centrality in G, strictly improves the regret. Finally, as a by-product of our analysis, we provide the first characterization of the minimax regret for bandit learning with delay.

Original languageEnglish
JournalJournal of Machine Learning Research
Volume20
StatePublished - 1 Feb 2019

Keywords

  • Cooperative multi-agent systems
  • Distributed learning
  • LOCAL communication
  • Multi-armed bandits
  • Regret minimization

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