Single Strategy

Single-strategy approaches, where a single method or decision rule is consistently applied, are being reevaluated across diverse fields. Current research focuses on improving single-strategy performance through techniques like curriculum learning (adapting training difficulty) and menu-based approaches (offering choices to agents), often leveraging reinforcement learning and graph neural networks. These advancements aim to address limitations of fixed strategies, particularly in complex scenarios like job scheduling and human-AI collaboration, leading to more efficient and adaptable systems. The ultimate goal is to enhance the effectiveness and robustness of single-strategy methods, improving outcomes in various applications.