Auction-based bidding among selfish local policies in a general-sum Markov game enables dynamic adaptation to evolving multi-objectives in reinforcement learning with Nash equilibrium guarantees.
Markov games as a framework for multi-agent reinforcement learning
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Auction-Based Online Policy Adaptation for Evolving Objectives
Auction-based bidding among selfish local policies in a general-sum Markov game enables dynamic adaptation to evolving multi-objectives in reinforcement learning with Nash equilibrium guarantees.