SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits

Abstract

Motivated by cognitive radio networks, we consider the stochastic multiplayer multi-armed bandit problem, where several players pull arms simultaneously and collisions occur if one of them is pulled by several players at the same stage. We present a decentralized algorithm that achieves the same performance as a centralized one, contradicting the existing lower bounds for that problem. This is possible by "hacking" the standard model by constructing a communication protocol between players that deliberately enforces collisions, allowing them to share their information at a negligible cost. This motivates the introduction of a more appropriate dynamic setting without sensing, where similar communication protocols are no longer possible. However, we show that the logarithmic growth of the regret is still achievable for this model with a new algorithm.

References in corpus (4)

• A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players
• Multi-Player Bandits Revisited
• Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without
• An Optimal Algorithm for Multiplayer Multi-Armed Bandits

Cited by in corpus (6)

• Multiplayer Bandit Learning, from Competition to Cooperation
• Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without
• An Optimal Algorithm for Multiplayer Multi-Armed Bandits
• Conservative Contextual Combinatorial Cascading Bandit
• Accelerated learning from recommender systems using multi-armed bandit
• Coordination without communication: optimal regret in two players multi-armed bandits