Robust and adaptive decision-making: a reinforcement learning perspective - Xue, Wanqi (Nanyang Technological University, 2024)
How to make decisions in complex and uncertain environments is a challenging and crucial task. Adversaries and perturbations in these environments disrupt existing policies, while the dynamic nature of the environments renders policies obsolete. Therefore, it is vital to learn robust policies capable of maintaining optimal performance in extreme scenarios and quickly adapting to changes. In this thesis, we focus on three real-world problem domains: network security games (NSGs), inter-agent communication and sequential recommendation. All of these domains necessitates robust and adaptive decision-making.