ReLExS: Reinforcement Learning Explanations for Stackelberg No-Regret Learners
On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 25 references that are in the map. First posted on arXiv 26 Aug 2024.
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8 of 25 references.
References · 8
- 2019Strategizing against No-regret Learnerscited by 50
- 2022Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Trainingcited by 28
- 2021Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers?cited by 24
- 2018M3RL: Mind-aware Multi-agent Management Reinforcement Learningcited by 22
- 2021Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learningcited by 19
- 2022Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learningcited by 12
- 2021Solving Structured Hierarchical Games Using Differential Backward Inductioncited by 3
- 2024The computation of approximate feedback Stackelberg equilibria in multi-player nonlinear constrained dynamic gamescited by 2
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- 2023Stackelberg Batch Policy Learning
- 2025Learning in Stackelberg Mean Field Games: A Non-Asymptotic Analysis
- 2025Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game
- 2026Finite-Time Analysis of Q-Value Iteration for General-Sum Stackelberg Games
- 2024widetildeO(T-1) Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games
- 2024Convex-Concave Zero-sum Markov Stackelberg Games
- 2024Bandits with Preference Feedback: A Stackelberg Game Perspective
- 2024Policy Iteration for Two-Player General-Sum Stochastic Stackelberg Games