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.
The max-min formulation of multi-objective reinforcement learning: From theory to a model-free algorithm
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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.