ReLExS: Reinforcement Learning Explanations for Stackelberg No-Regret Learners
<|reference_start|>ReLExS: Reinforcement Learning Explanations for Stackelberg No-Regret Learners: 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.<|reference_end|>
@article{huang2024relexs:,
    title={ReLExS: Reinforcement Learning Explanations for Stackelberg No-Regret
  Learners},
    author={Xiangge Huang, Jingyuan Li, Jiaqing Xie},
    journal={arXiv preprint arXiv:2408.14086},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14086},
    primaryClass={cs.GT cs.LG}
}