# Concurrent bandits and cognitive radio networks

arXiv: https://arxiv.org/abs/1404.5421 · HTML: https://mlsanity.com/paper/1404.5421/

## Key facts

- Cited by 23 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 22 Apr 2014.

## Built on by (most cited first, 23 of 23)

- [Federated Linear Contextual Bandits](https://mlsanity.com/paper/2110.14177/) (2021)
- [Federated Multi-Armed Bandits](https://mlsanity.com/paper/2101.12204/) (2021)
- [On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2110.05707/) (2021)
- [Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization](https://mlsanity.com/paper/2110.14622/) (2021)
- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [Online Learning for Cooperative Multi-Player Multi-Armed Bandits](https://mlsanity.com/paper/2109.03818/) (2021)
- [Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions](https://mlsanity.com/paper/2106.13669/) (2021)
- [Optimal Cooperative Multiplayer Learning Bandits with Noisy Rewards and No Communication](https://mlsanity.com/paper/2311.06210/) (2023)
- [An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit](https://mlsanity.com/paper/2111.04873/) (2021)
- [Dynamic Spectrum Access using Stochastic Multi-User Bandits](https://mlsanity.com/paper/2101.04388/) (2021)
- [Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits](https://mlsanity.com/paper/2305.18784/) (2023)
- [Stochastic Bandits for Egalitarian Assignment](https://mlsanity.com/paper/2410.05856/) (2024)
- [A High Performance, Low Complexity Algorithm for Multi-Player Bandits Without Collision Sensing Information](https://mlsanity.com/paper/2102.10200/) (2021)
- [Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2111.10933/) (2021)
- [The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication](https://mlsanity.com/paper/2202.09653/) (2022)
- [Multi-Player Bandits Robust to Adversarial Collisions](https://mlsanity.com/paper/2211.07817/) (2022)
- [United We Stand: Decentralized Multi-Agent Planning With Attrition](https://mlsanity.com/paper/2407.08254/) (2024)
- [Competing Bandits in Decentralized Contextual Matching Markets](https://mlsanity.com/paper/2411.11794/) (2024)
- [Constant or logarithmic regret in asynchronous multiplayer bandits](https://mlsanity.com/paper/2305.19691/) (2023)
- [Near-Optimal Privacy-Preserving Learning for Max-Min Fair Multi-Agent Bandits](https://mlsanity.com/paper/2306.04498/) (2023)
- [Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities](https://mlsanity.com/paper/2408.10865/) (2024)
- [Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedback](https://mlsanity.com/paper/2602.16183/) (2026)
- [Regret Analysis of Sleeping Competing Bandits](https://mlsanity.com/paper/2603.19700/) (2026)

## Abstract (arXiv)

We consider the problem of multiple users targeting the arms of a single multi-armed stochastic bandit. The motivation for this problem comes from cognitive radio networks, where selfish users need to coexist without any side communication between them, implicit cooperation or common control. Even the number of users may be unknown and can vary as users join or leave the network. We propose an algorithm that combines an ε-greedy learning rule with a collision avoidance mechanism. We analyze its regret with respect to the system-wide optimum and show that sub-linear regret can be obtained in this setting. Experiments show dramatic improvement compared to other algorithms for this setting.