# Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions

@inproceedings{Bubeck2021CooperativeAS, title={Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions}, author={S'ebastien Bubeck and Thomas Budzinski and Mark Sellke}, booktitle={COLT}, year={2021} }

We consider the cooperative multi-player version of the stochastic multi-armed bandit problem. We study the regime where the players cannot communicate but have access to shared randomness. In prior work by the first two authors, a strategy for this regime was constructed for two players and three arms, with regret $\tilde{O}(\sqrt{T})$, and with no collisions at all between the players (with very high probability). In this paper we show that these properties (near-optimal regret and no…

## 7 Citations

Multi-Player Multi-Armed Bandits With Collision-Dependent Reward Distributions

- Computer Science, EngineeringIEEE Transactions on Signal Processing
- 2021

The Error-Correction Collision Communication (EC3) algorithm is proposed that models implicit communication as a reliable communication over noisy channel problem, for which random coding error exponent is used to establish the optimal regret that no communication protocol can beat.

An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit

- Computer Science, MathematicsArXiv
- 2021

This work shows that a simple modification to a successive elimination strategy can be used to allow the players to estimate their suboptimality gaps, up to constant factors, in the absence of collisions, and designs a communication protocol that successfully uses the small reward of collisions to coordinate among players, while preserving meaningful instance-dependent logarithmic regret guarantees.

Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization

- Computer Science, MathematicsArXiv
- 2021

BEACON bridges the algorithm design and regret analysis of combinatorial MAB (CMAB) and MP-MAB, two largely disjointed areas in MAB, and the results suggest that this previously ignored connection is worth further investigation.

Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure

- Computer Science, MathematicsArXiv
- 2021

This work considers two-agent multi-armed bandits and Markov decision processes with a hierarchical information structure arising in applications to propose simpler and more efficient algorithms that require no coordination or communication.

Decentralized Learning in Online Queuing Systems

- Computer Science, MathematicsArXiv
- 2021

Cooperative queues are considered and the first learning decentralized algorithm guaranteeing stability of the system as long as the ratio of rates is larger than 1 is proposed, thus reaching performances comparable to centralized strategies.

Collaborative Pure Exploration in Kernel Bandit

- Computer ScienceArXiv
- 2021

In this paper, we formulate a Collaborative Pure Exploration in Kernel Bandit problem (CoPE-KB), which provides a novel model for multi-agent multi-task decision making under limited communication…

Bandit Learning in Decentralized Matching Markets

- Computer Science, MathematicsArXiv
- 2020

This model extends the standard stochastic multi-armed bandit framework to a decentralized multiple player setting with competition and introduces a new algorithm for this setting that attains stable regret when preferences of the arms over players are shared.

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