# Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits

arXiv: https://arxiv.org/abs/2212.06279 · HTML: https://mlsanity.com/paper/2212.06279/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 67 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 28 of 28)

- [Stochastic bandits robust to adversarial corruptions](https://mlsanity.com/paper/1803.09353/) (2018)
- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [Combinatorial Sleeping Bandits with Fairness Constraints](https://mlsanity.com/paper/1901.04891/) (2019)
- [Federated Multi-Armed Bandits](https://mlsanity.com/paper/2101.12204/) (2021)
- [Federated Bandit: A Gossiping Approach](https://mlsanity.com/paper/2010.12763/) (2020)
- [Stochastic Bandit Models for Delayed Conversions](https://mlsanity.com/paper/1706.09186/) (2017)
- [Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithms](https://mlsanity.com/paper/1606.00911/) (2016)
- [Social Learning in Multi Agent Multi Armed Bandits](https://mlsanity.com/paper/1910.02100/) (2019)
- [SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits](https://mlsanity.com/paper/1809.08151/) (2018)
- [Distributed Exploration in Multi-Armed Bandits](https://mlsanity.com/paper/1311.0800/) (2013)
- [Multi-Armed Bandits with Correlated Arms](https://mlsanity.com/paper/1911.03959/) (2019)
- [Cooperative Multi-Agent Bandits with Heavy Tails](https://mlsanity.com/paper/2008.06244/) (2020)
- [Collaborative Learning of Stochastic Bandits over a Social Network](https://mlsanity.com/paper/1602.08886/) (2016)
- [What Will 5G Be?](https://mlsanity.com/paper/1405.2957/) (2014)
- [A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players](https://mlsanity.com/paper/1902.01239/) (2019)
- [Decentralized Multi-player Multi-armed Bandits with No Collision Information](https://mlsanity.com/paper/2003.00162/) (2020)
- [Kernel Methods for Cooperative Multi-Agent Contextual Bandits](https://mlsanity.com/paper/2008.06220/) (2020)
- [Multiplayer bandits without observing collision information](https://mlsanity.com/paper/1808.08416/) (2018)
- [Robust Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2007.03812/) (2020)
- [Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization](https://mlsanity.com/paper/2110.14622/) (2021)
- [One More Step Towards Reality: Cooperative Bandits with Imperfect Communication](https://mlsanity.com/paper/2111.12482/) (2021)
- [Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without](https://mlsanity.com/paper/1904.12233/) (2019)
- [Multi-Player Bandits: A Trekking Approach](https://mlsanity.com/paper/1809.06040/) (2018)
- [Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching](https://mlsanity.com/paper/2001.03835/) (2020)
- [Decentralized Multi-Agent Linear Bandits with Safety Constraints](https://mlsanity.com/paper/2012.00314/) (2020)
- [An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit](https://mlsanity.com/paper/2111.04873/) (2021)
- [Efficient Learning-based Scheduling for Information Freshness in Wireless Networks](https://mlsanity.com/paper/2101.00257/) (2021)
- [Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous Networks](https://mlsanity.com/paper/1901.03868/) (2019)

## Built on by (most cited first, 2 of 2)

- [Competitive Multi-armed Bandit Games for Resource Sharing](https://mlsanity.com/paper/2503.20975/) (2025)
- [Decentralized Asynchronous Multi-player Bandits](https://mlsanity.com/paper/2509.25824/) (2025)

## Abstract (arXiv)

Multi-player multi-armed bandit is an increasingly relevant decision-making problem, motivated by applications to cognitive radio systems. Most research for this problem focuses exclusively on the settings that players have textitfull access to all arms and receive no reward when pulling the same arm. Hence all players solve the same bandit problem with the goal of maximizing their cumulative reward. However, these settings neglect several important factors in many real-world applications, where players have textitlimited access to textita dynamic local subset of arms (i.e., an arm could sometimes be ``walking'' and not accessible to the player). To this end, this paper proposes a textitmulti-player multi-armed walking bandits model, aiming to address aforementioned modeling issues. The goal now is to maximize the reward, however, players can only pull arms from the local subset and only collect a full reward if no other players pull the same arm. We adopt Upper Confidence Bound (UCB) to deal with the exploration-exploitation tradeoff and employ distributed optimization techniques to properly handle collisions. By carefully integrating these two techniques, we propose a decentralized algorithm with near-optimal guarantee on the regret, and can be easily implemented to obtain competitive empirical performance.