ReLExS: Reinforcement Learning Explanations for Stackelberg No-Regret Learners
Huang, Xiangge, Li, Jingyuan, Xie, Jiaqing
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Aug-26-2024
- Country:
- Europe
- Switzerland > Zürich
- Zürich (0.04)
- France > Île-de-France
- Switzerland > Zürich
- Asia > Middle East
- Jordan (0.04)
- Europe
- Genre:
- Research Report (0.40)
- Industry:
- Leisure & Entertainment > Games (1.00)
- Technology: