# On Distributed Cooperative Decision-Making in Multiarmed Bandits

arXiv: https://arxiv.org/abs/1512.06888 · HTML: https://mlsanity.com/paper/1512.06888/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2015.

## Built on by (most cited first, 30 of 33)

- [Federated Multi-armed Bandits with Personalization](https://mlsanity.com/paper/2102.13101/) (2021)
- [Federated Linear Contextual Bandits](https://mlsanity.com/paper/2110.14177/) (2021)
- [Federated Multi-Armed Bandits](https://mlsanity.com/paper/2101.12204/) (2021)
- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (2022)
- [Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation](https://mlsanity.com/paper/2103.04972/) (2021)
- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [One More Step Towards Reality: Cooperative Bandits with Imperfect Communication](https://mlsanity.com/paper/2111.12482/) (2021)
- [Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost](https://mlsanity.com/paper/2205.13170/) (2022)
- [Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards](https://mlsanity.com/paper/2306.05579/) (2023)
- [Private and Byzantine-Proof Cooperative Decision-Making](https://mlsanity.com/paper/2205.14174/) (2022)
- [Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions](https://mlsanity.com/paper/2106.13669/) (2021)
- [When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Bandits](https://mlsanity.com/paper/2110.04396/) (2021)
- [Distributed Linear Bandits under Communication Constraints](https://mlsanity.com/paper/2211.02212/) (2022)
- [Linear Contextual Bandits with Interference](https://mlsanity.com/paper/2409.15682/) (2024)
- [Federated Multi-Armed Bandits Under Byzantine Attacks](https://mlsanity.com/paper/2205.04134/) (2022)
- [Collaborative Learning in Kernel-based Bandits for Distributed Users](https://mlsanity.com/paper/2207.07948/) (2022)
- [Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions](https://mlsanity.com/paper/2106.04207/) (2021)
- [Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2111.10933/) (2021)
- [Incentivized Communication for Federated Bandits](https://mlsanity.com/paper/2309.11702/) (2023)
- [Byzantine-Resilient Decentralized Multi-Armed Bandits](https://mlsanity.com/paper/2310.07320/) (2023)
- [Multiplayer Information Asymmetric Bandits in Metric Spaces](https://mlsanity.com/paper/2503.08004/) (2025)
- [Multiplayer Information Asymmetric Contextual Bandits](https://mlsanity.com/paper/2503.08961/) (2025)
- [Incentivized Truthful Communication for Federated Bandits](https://mlsanity.com/paper/2402.04485/) (2024)
- [Regret Lower Bounds in Multi-agent Multi-armed Bandit](https://mlsanity.com/paper/2308.08046/) (2023)
- [Harnessing the Power of Federated Learning in Federated Contextual Bandits](https://mlsanity.com/paper/2312.16341/) (2023)
- [Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit](https://mlsanity.com/paper/2402.04417/) (2024)
- [Laplacian Kernelized Bandit](https://mlsanity.com/paper/2601.00461/) (2026)
- [Multi-Objective Multi-Agent Bandits: From Learning Efficiency to Fairness Optimization](https://mlsanity.com/paper/2605.06864/) (2026)
- [Constant or logarithmic regret in asynchronous multiplayer bandits](https://mlsanity.com/paper/2305.19691/) (2023)
- [Distributed Optimization via Kernelized Multi-armed Bandits](https://mlsanity.com/paper/2312.04719/) (2023)

## Abstract (arXiv)

We study the explore-exploit tradeoff in distributed cooperative decision-making using the context of the multiarmed bandit (MAB) problem. For the distributed cooperative MAB problem, we design the cooperative UCB algorithm that comprises two interleaved distributed processes: (i) running consensus algorithms for estimation of rewards, and (ii) upper-confidence-bound-based heuristics for selection of arms. We rigorously analyze the performance of the cooperative UCB algorithm and characterize the influence of communication graph structure on the decision-making performance of the group.