# A survey on multi-player bandits

arXiv: https://arxiv.org/abs/2211.16275 · HTML: https://mlsanity.com/paper/2211.16275/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 135 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 30 of 53)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Thompson Sampling for Combinatorial Semi-Bandits](https://mlsanity.com/paper/1803.04623/) (2018)
- [Delay and Cooperation in Nonstochastic Bandits](https://mlsanity.com/paper/1602.04741/) (2016)
- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [On Explore-Then-Commit Strategies](https://mlsanity.com/paper/1605.08988/) (2016)
- [SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits](https://mlsanity.com/paper/1809.08151/) (2018)
- [Bandit Learning in Decentralized Matching Markets](https://mlsanity.com/paper/2012.07348/) (2020)
- [Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays](https://mlsanity.com/paper/1506.00779/) (2015)
- [Learning Equilibria in Matching Markets from Bandit Feedback](https://mlsanity.com/paper/2108.08843/) (2021)
- [On Distributed Cooperative Decision-Making in Multiarmed Bandits](https://mlsanity.com/paper/1512.06888/) (2015)
- [Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits](https://mlsanity.com/paper/2006.06613/) (2020)
- [Beyond log2(T) Regret for Decentralized Bandits in Matching Markets](https://mlsanity.com/paper/2103.07501/) (2021)
- [The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms](https://mlsanity.com/paper/2002.10121/) (2020)
- [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)
- [Concurrent bandits and cognitive radio networks](https://mlsanity.com/paper/1404.5421/) (2014)
- [Combinatorial semi-bandit with known covariance](https://mlsanity.com/paper/1612.01859/) (2016)
- [Cooperative Online Learning: Keeping your Neighbors Updated](https://mlsanity.com/paper/1901.08082/) (2019)
- [On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits](https://mlsanity.com/paper/1505.00553/) (2015)
- [Multiplayer bandits without observing collision information](https://mlsanity.com/paper/1808.08416/) (2018)
- [Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits](https://mlsanity.com/paper/1907.03346/) (2019)
- [Robust Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2007.03812/) (2020)
- [Regret, stability & fairness in matching markets with bandit learners](https://mlsanity.com/paper/2102.06246/) (2021)
- [Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization](https://mlsanity.com/paper/2110.14622/) (2021)
- [Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings](https://mlsanity.com/paper/1807.00491/) (2018)
- [Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without](https://mlsanity.com/paper/1904.12233/) (2019)
- [Selfish Robustness and Equilibria in Multi-Player Bandits](https://mlsanity.com/paper/2002.01197/) (2020)
- [Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions](https://mlsanity.com/paper/2011.03896/) (2020)
- [Distributed Learning over Markovian Fading Channels for Stable Spectrum Access](https://mlsanity.com/paper/2101.11292/) (2021)

## Built on by (most cited first, 19 of 19)

- [Incentivized Learning in Principal-Agent Bandit Games](https://mlsanity.com/paper/2403.03811/) (2024)
- [Competing for Shareable Arms in Multi-Player Multi-Armed Bandits](https://mlsanity.com/paper/2305.19158/) (2023)
- [Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?](https://mlsanity.com/paper/2507.11891/) (2025)
- [Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality](https://mlsanity.com/paper/2406.19824/) (2024)
- [Multi-Player Approaches for Dueling Bandits](https://mlsanity.com/paper/2405.16168/) (2024)
- [KernelBand: Steering LLM-based Kernel Optimization via Hardware-Aware Multi-Armed Bandits](https://mlsanity.com/paper/2511.18868/) (2025)
- [GROS: A General Robust Aggregation Strategy](https://mlsanity.com/paper/2402.15442/) (2024)
- [QuACK: A Multipurpose Queuing Algorithm for Cooperative k-Armed Bandits](https://mlsanity.com/paper/2410.23867/) (2024)
- [Competing Bandits in Decentralized Contextual Matching Markets](https://mlsanity.com/paper/2411.11794/) (2024)
- [Bandit Max-Min Fair Allocation](https://mlsanity.com/paper/2505.05169/) (2025)
- [Multi-Agent Lipschitz Bandits](https://mlsanity.com/paper/2602.16965/) (2026)
- [Regret Analysis of Sleeping Competing Bandits](https://mlsanity.com/paper/2603.19700/) (2026)
- [Constant or logarithmic regret in asynchronous multiplayer bandits](https://mlsanity.com/paper/2305.19691/) (2023)
- [PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators](https://mlsanity.com/paper/2403.15524/) (2024)
- [In-Domain African Languages Translation Using LLMs and Multi-armed Bandits](https://mlsanity.com/paper/2505.15069/) (2025)
- [Decentralized Asynchronous Multi-player Bandits](https://mlsanity.com/paper/2509.25824/) (2025)
- [Scaling Federated Linear Contextual Bandits via Sketching](https://mlsanity.com/paper/2605.00500/) (2026)
- [Coordinating the Unknown Lipschitz Constant in Multiplayer Bandits](https://mlsanity.com/paper/2608.10526/) (2026)
- [Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Bandits](https://mlsanity.com/paper/2610.18514/) (2026)

## Abstract (arXiv)

Due mostly to its application to cognitive radio networks, multiplayer bandits gained a lot of interest in the last decade. A considerable progress has been made on its theoretical aspect. However, the current algorithms are far from applicable and many obstacles remain between these theoretical results and a possible implementation of multiplayer bandits algorithms in real cognitive radio networks. This survey contextualizes and organizes the rich multiplayer bandits literature. In light of the existing works, some clear directions for future research appear. We believe that a further study of these different directions might lead to theoretical algorithms adapted to real-world situations.