Kernel Methods for Cooperative Multi-Agent Contextual Bandits

Abhimanyu Dubey, Alex ‘Sandy’ Pentland
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2740-2750, 2020.

Abstract

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 Coop-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 Coop-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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-dubey20b, title = {Kernel Methods for Cooperative Multi-Agent Contextual Bandits}, author = {Dubey, Abhimanyu and Pentland, Alex `Sandy'}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {2740--2750}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/dubey20b/dubey20b.pdf}, url = {http://proceedings.mlr.press/v119/dubey20b.html}, abstract = {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 Coop-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 Coop-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.} }
Endnote
%0 Conference Paper %T Kernel Methods for Cooperative Multi-Agent Contextual Bandits %A Abhimanyu Dubey %A Alex ‘Sandy’ Pentland %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-dubey20b %I PMLR %P 2740--2750 %U http://proceedings.mlr.press/v119/dubey20b.html %V 119 %X 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 Coop-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 Coop-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.
APA
Dubey, A. & Pentland, A.‘.. (2020). Kernel Methods for Cooperative Multi-Agent Contextual Bandits. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:2740-2750 Available from http://proceedings.mlr.press/v119/dubey20b.html.

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