# Kernel Methods for Cooperative Multi-Agent Contextual Bandits

arXiv: https://arxiv.org/abs/2008.06220 · HTML: https://mlsanity.com/paper/2008.06220/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 21 of 21)

- [Adaptive Methods for Real-World Domain Generalization](https://mlsanity.com/paper/2103.15796/) (2021)
- [Hierarchical Bayesian Bandits](https://mlsanity.com/paper/2111.06929/) (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)
- [Communication Efficient Distributed Learning for Kernelized Contextual Bandits](https://mlsanity.com/paper/2206.04835/) (2022)
- [Collaborative Pure Exploration in Kernel Bandit](https://mlsanity.com/paper/2110.15771/) (2021)
- [Linear Stochastic Bandits over a Bit-Constrained Channel](https://mlsanity.com/paper/2203.01198/) (2022)
- [Differentially Private Linear Bandits with Partial Distributed Feedback](https://mlsanity.com/paper/2207.05827/) (2022)
- [Linear Contextual Bandits with Interference](https://mlsanity.com/paper/2409.15682/) (2024)
- [Collaborative Learning in Kernel-based Bandits for Distributed Users](https://mlsanity.com/paper/2207.07948/) (2022)
- [Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds](https://mlsanity.com/paper/2206.02834/) (2022)
- [Cooperative Online Learning with Feedback Graphs](https://mlsanity.com/paper/2106.04982/) (2021)
- [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)
- [Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling](https://mlsanity.com/paper/2205.10113/) (2022)
- [Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits](https://mlsanity.com/paper/2212.06279/) (2022)
- [Laplacian Kernelized Bandit](https://mlsanity.com/paper/2601.00461/) (2026)
- [Cooperative Thresholded Lasso for Sparse Linear Bandit](https://mlsanity.com/paper/2305.19161/) (2023)
- [Distributed Optimization via Kernelized Multi-armed Bandits](https://mlsanity.com/paper/2312.04719/) (2023)
- [Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness](https://mlsanity.com/paper/2402.13182/) (2024)
- [Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedback](https://mlsanity.com/paper/2602.16183/) (2026)

## Abstract (arXiv)

Cooperative multi-agent decision making involves a group of agents cooperatively solving learning problems while communicating over a network with delays. In this paper, we consider the kernelised contextual bandit problem, where the reward obtained by an agent is an arbitrary linear function of the contexts' images in the related reproducing kernel Hilbert space (RKHS), and a group of agents must cooperate to collectively solve their unique decision problems. For this problem, we propose textscCoop-KernelUCB, an algorithm that provides near-optimal bounds on the per-agent regret, and is both computationally and communicatively efficient. For special cases of the cooperative problem, we also provide variants of textscCoop-KernelUCB that provides optimal per-agent regret. In addition, our algorithm generalizes several existing results in the multi-agent bandit setting. Finally, on a series of both synthetic and real-world multi-agent network benchmarks, we demonstrate that our algorithm significantly outperforms existing benchmarks.