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.
|Journal||Journal of Machine Learning Research|
|State||Published - 1 Feb 2019|
- Cooperative multi-agent systems
- Distributed learning
- LOCAL communication
- Multi-armed bandits
- Regret minimization