Meta Stackelberg

Meta-Stackelberg methods address sequential decision-making problems where a leader agent anticipates and influences the actions of one or more follower agents. Current research focuses on developing efficient algorithms, such as meta-learning approaches, to learn optimal leader strategies in dynamic environments with incomplete information about follower behavior, often modeled as Stackelberg games or Markov games. These techniques find applications in diverse fields, including robotics, federated learning security, and adaptive systems, offering improved robustness and adaptability compared to traditional methods. The resulting advancements contribute to more effective and efficient control in complex, interactive systems.