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Kernel Methods for Cooperative Multi-Agent Contextual Bandits
Abhimanyu Dubey · Alex `Sandy' Pentland

Tue Jul 14 11:00 AM -- 11:45 AM & Tue Jul 14 10:00 PM -- 10:45 PM (PDT) @

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.

Author Information

Abhimanyu Dubey (Massachusetts Institute of Technology)

I am a PhD student in the Human Dynamics group at MIT, advised by Professor Alex Pentland. My research interests are in robust and cooperative machine learning, including problems in multi-agent decision-making and transfer learning. Prior to this, I received a master's degree in Computer Science and bachelor's degree in Electrical Engineering at IIT Delhi, where I was advised by Professor Sumeet Agarwal. I've also spent time as a research intern at Facebook AI, and was a post-baccalaureate fellow at the Department of Economics at Harvard, under Professor Ed Glaeser. My research has been supported by a Snap Research Scholarship (2019) and an Emerging Worlds Fellowship (2017).

Alex `Sandy' Pentland (MIT)

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