# Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access

arXiv: https://arxiv.org/abs/1910.09091 · HTML: https://mlsanity.com/paper/1910.09091/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

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

- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization](https://mlsanity.com/paper/2110.14622/) (2021)
- [Distributed Bandits with Heterogeneous Agents](https://mlsanity.com/paper/2201.09353/) (2022)
- [Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions](https://mlsanity.com/paper/2106.13669/) (2021)
- [Dynamic Spectrum Access using Stochastic Multi-User Bandits](https://mlsanity.com/paper/2101.04388/) (2021)
- [Learning to Control Unknown Strongly Monotone Games](https://mlsanity.com/paper/2407.00575/) (2024)
- [Multi-Player Bandits Robust to Adversarial Collisions](https://mlsanity.com/paper/2211.07817/) (2022)
- [Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs](https://mlsanity.com/paper/2312.15549/) (2023)
- [Constant or logarithmic regret in asynchronous multiplayer bandits](https://mlsanity.com/paper/2305.19691/) (2023)
- [Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities](https://mlsanity.com/paper/2408.10865/) (2024)

## Abstract (arXiv)

Multi-user multi-armed bandits have emerged as a good model for uncoordinated spectrum access problems. In this paper we consider the scenario where users cannot communicate with each other. In addition, the environment may appear differently to different users, i.e., the mean rewards as observed by different users for the same channel may be different. With this setup, we present a policy that achieves a regret of O (logT). This paper has been accepted at Asilomar Conference on Signals, Systems, and Computers 2019.