Distributed No-Regret Learning in Multi-Agent Systems

• URL: http://arxiv.org/abs/2002.09047v1
• Date: Thu, 20 Feb 2020 22:30:17 GMT
• Title: Distributed No-Regret Learning in Multi-Agent Systems
• Authors: Xiao Xu, Qing Zhao
• Abstract summary: Four emerging game characteristics challenge canonical game models are explored. For each of the four characteristics, we illuminate its implications and ramifications in game modeling, notions of regret, feasible game outcomes, and the design and analysis of distributed learning algorithms.
• Score: 12.111429383532888
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this tutorial article, we give an overview of new challenges and representative results on distributed no-regret learning in multi-agent systems modeled as repeated unknown games. Four emerging game characteristics---dynamicity, incomplete and imperfect feedback, bounded rationality, and heterogeneity---that challenge canonical game models are explored. For each of the four characteristics, we illuminate its implications and ramifications in game modeling, notions of regret, feasible game outcomes, and the design and analysis of distributed learning algorithms.

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