# Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithms

arXiv: https://arxiv.org/abs/1606.00911 · HTML: https://mlsanity.com/paper/1606.00911/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2016.

## Built on by (most cited first, 30 of 48)

- [Multi-Agent Multi-Armed Bandits with Limited Communication](https://mlsanity.com/paper/2102.08462/) (2021)
- [Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation](https://mlsanity.com/paper/2103.04972/) (2021)
- [Exploiting Heterogeneity in Robust Federated Best-Arm Identification](https://mlsanity.com/paper/2109.05700/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [One More Step Towards Reality: Cooperative Bandits with Imperfect Communication](https://mlsanity.com/paper/2111.12482/) (2021)
- [Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance](https://mlsanity.com/paper/2402.01096/) (2024)
- [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)
- [Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications](https://mlsanity.com/paper/2204.13502/) (2022)
- [Private and Byzantine-Proof Cooperative Decision-Making](https://mlsanity.com/paper/2205.14174/) (2022)
- [An Efficient Algorithm for Fair Multi-Agent Multi-Armed Bandit with Low Regret](https://mlsanity.com/paper/2209.11817/) (2022)
- [Distributed Bandits with Heterogeneous Agents](https://mlsanity.com/paper/2201.09353/) (2022)
- [Linear Stochastic Bandits over a Bit-Constrained Channel](https://mlsanity.com/paper/2203.01198/) (2022)
- [When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Bandits](https://mlsanity.com/paper/2110.04396/) (2021)
- [On-Demand Communication for Asynchronous Multi-Agent Bandits](https://mlsanity.com/paper/2302.07446/) (2023)
- [Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions](https://mlsanity.com/paper/2106.04207/) (2021)
- [Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds](https://mlsanity.com/paper/2206.02834/) (2022)
- [Robust Online and Distributed Mean Estimation Under Adversarial Data Corruption](https://mlsanity.com/paper/2209.09624/) (2022)
- [Multi-Robot Gaussian Process Estimation and Coverage: A Deterministic Sequencing Algorithm and Regret Analysis](https://mlsanity.com/paper/2101.04306/) (2021)
- [Collaborative Multi-agent Stochastic Linear Bandits](https://mlsanity.com/paper/2205.06331/) (2022)
- [Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits](https://mlsanity.com/paper/2305.18784/) (2023)
- [Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2111.10933/) (2021)
- [On Regret-optimal Cooperative Nonstochastic Multi-armed Bandits](https://mlsanity.com/paper/2211.17154/) (2022)
- [Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits](https://mlsanity.com/paper/2301.11442/) (2023)
- [Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits](https://mlsanity.com/paper/2212.06279/) (2022)
- [Cooperative Multi-Agent Constrained Stochastic Linear Bandits](https://mlsanity.com/paper/2410.17382/) (2024)
- [Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams](https://mlsanity.com/paper/2110.00751/) (2021)
- [Anonymous Bandits for Multi-User Systems](https://mlsanity.com/paper/2210.12198/) (2022)
- [Regret Lower Bounds in Multi-agent Multi-armed Bandit](https://mlsanity.com/paper/2308.08046/) (2023)
- [Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs](https://mlsanity.com/paper/2312.15549/) (2023)

## Abstract (arXiv)

We study distributed cooperative decision-making under the explore-exploit tradeoff in the multiarmed bandit (MAB) problem. We extend the state-of-the-art frequentist and Bayesian algorithms for single-agent MAB problems to cooperative distributed algorithms for multi-agent MAB problems in which agents communicate according to a fixed network graph. We rely on a running consensus algorithm for each agent's estimation of mean rewards from its own rewards and the estimated rewards of its neighbors. We prove the performance of these algorithms and show that they asymptotically recover the performance of a centralized agent. Further, we rigorously characterize the influence of the communication graph structure on the decision-making performance of the group.