# Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications

arXiv: https://arxiv.org/abs/2204.13502 · HTML: https://mlsanity.com/paper/2204.13502/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithms](https://mlsanity.com/paper/1606.00911/) (2016)
- [SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits](https://mlsanity.com/paper/1809.08151/) (2018)
- [A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players](https://mlsanity.com/paper/1902.01239/) (2019)
- [Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization](https://mlsanity.com/paper/2110.14622/) (2021)
- [Selfish Robustness and Equilibria in Multi-Player Bandits](https://mlsanity.com/paper/2002.01197/) (2020)
- [Decentralized Heterogeneous Multi-Player Multi-Armed Bandits with Non-Zero Rewards on Collisions](https://mlsanity.com/paper/1910.09089/) (2019)
- [A First Look at Commercial 5G Performance on Smartphones](https://mlsanity.com/paper/1909.07532/) (2019)

## Built on by (most cited first, 10 of 10)

- [Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms](https://mlsanity.com/paper/2206.08776/) (2022)
- [Competing for Shareable Arms in Multi-Player Multi-Armed Bandits](https://mlsanity.com/paper/2305.19158/) (2023)
- [Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints](https://mlsanity.com/paper/2401.11563/) (2024)
- [Federated Learning for Heterogeneous Bandits with Unobserved Contexts](https://mlsanity.com/paper/2303.17043/) (2023)
- [Meet Me at the Arm: The Cooperative Multi-Armed Bandits Problem with Shareable Arms](https://mlsanity.com/paper/2506.10127/) (2025)
- [PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators](https://mlsanity.com/paper/2403.15524/) (2024)
- [Decentralized Asynchronous Multi-player Bandits](https://mlsanity.com/paper/2509.25824/) (2025)
- [Multiple-play Stochastic Bandits with Prioritized Arm Capacity Sharing](https://mlsanity.com/paper/2512.21626/) (2025)
- [The Price of Decentralization in Top-K Arm Identification](https://mlsanity.com/paper/2608.22120/) (2026)
- [ICMAPE: In-Context Multiagent Pure Exploration](https://mlsanity.com/paper/2609.33986/) (2026)

## Abstract (arXiv)

Multi-player multi-armed bandits (MMAB) study how decentralized players cooperatively play the same multi-armed bandit so as to maximize their total cumulative rewards. Existing MMAB models mostly assume when more than one player pulls the same arm, they either have a collision and obtain zero rewards, or have no collision and gain independent rewards, both of which are usually too restrictive in practical scenarios. In this paper, we propose an MMAB with shareable resources as an extension to the collision and non-collision settings. Each shareable arm has finite shareable resources and a "per-load" reward random variable, both of which are unknown to players. The reward from a shareable arm is equal to the "per-load" reward multiplied by the minimum between the number of players pulling the arm and the arm's maximal shareable resources. We consider two types of feedback: sharing demand information (SDI) and sharing demand awareness (SDA), each of which provides different signals of resource sharing. We design the DPE-SDI and SIC-SDA algorithms to address the shareable arm problem under these two cases of feedback respectively and prove that both algorithms have logarithmic regrets that are tight in the number of rounds. We conduct simulations to validate both algorithms' performance and show their utilities in wireless networking and edge computing.