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ReLExS: Reinforcement Learning Explanations for Stackelberg No-Regret Learners

2024/08/26 by Jingyuan Li, Huang, Xiangge, Jiaqing Xie +2
Computer Science · #Explainable Artificial Intelligence (XAI) #Data Stream Mining Techniques #Reinforcement Learning in Robotics

paper · pdf · doi:10.48550/arxiv.2408.14086

Abstract

With the constraint of a no regret follower, will the players in a two-player Stackelberg game still reach Stackelberg equilibrium? We first show when the follower strategy is either reward-average or transform-reward-average, the two players can always get the Stackelberg Equilibrium. Then, we extend that the players can achieve the Stackelberg equilibrium in the two-player game under the no regret constraint. Also, we show a strict upper bound of the follower's utility difference between with and without no regret constraint. Moreover, in constant-sum two-player Stackelberg games with non-regret action sequences, we ensure the total optimal utility of the game remains also bounded.

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