Hybrid Reward

Hybrid reward structures in reinforcement learning and related fields aim to improve the efficiency and robustness of learning algorithms by combining different reward signals or models. Current research focuses on developing algorithms that effectively handle these hybrid structures, including modifications of existing methods like UCB and LinUCB, and the exploration of novel approaches such as those leveraging extreme value statistics or adversarial reward designs. This work is significant because it addresses limitations of simpler reward models, leading to improved performance in diverse applications such as online task scheduling, multi-agent systems, and market making, while also advancing theoretical understanding of regret bounds and convergence properties.