ReLExS: Reinforcement Learning Explanations for Stackelberg No-Regret Learners
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.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2024
- DOI:
- 10.48550/arXiv.2408.14086
- arXiv:
- arXiv:2408.14086
- Bibcode:
- 2024arXiv240814086H
- Keywords:
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- Computer Science - Computer Science and Game Theory;
- Computer Science - Machine Learning
- E-Print:
- 10 pages, 3 figures. Technical Report