A novel individually rational objective in multi-agent multi-armed bandits: Algorithms and regret bounds
Paper i proceeding, 2020

We study a two-player stochastic multi-armed bandit (MAB) problem with different expected rewards for each player, a generalisation of two-player general sum repeated games to stochastic rewards. Our aim is to find the egalitarian bargaining solution (EBS) for the repeated game, which can lead to much higher rewards than the maximin value of both players. Our main contribution is the derivation of an algorithm, UCRG, that achieves simultaneously for both players, a high-probability regret bound of order Õ ( T2/3) after any T rounds of play. We demonstrate that our upper bound is nearly optimal by proving a lower bound of Ω ( T2/3) for any algorithm. Experiments confirm our theoretical results and the superiority of UCRG compared to the well-known explore-then-commit heuristic.

Multi-armed bandits

Egalitarian bargaining solution


Individual rationality


Aristide Tossou

Chalmers, Data- och informationsteknik, Data Science

Christos Dimitrakakis

Universitetet i Oslo

Jaroslaw Rzepecki

Microsoft Research

K. Hofmann

Microsoft Research

Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

15488403 (ISSN) 15582914 (eISSN)

Vol. 2020-May 1395-1403
9781450375184 (ISBN)

19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
Virtual Auckland, New Zealand,





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