Reward Signal

Reward signals are crucial for guiding reinforcement learning (RL) agents towards desired behaviors, but designing effective reward functions remains a significant challenge. Current research focuses on automating reward design using large language models (LLMs) to interpret human preferences or demonstrations, developing novel reward shaping techniques to improve learning efficiency in sparse reward scenarios, and exploring alternative reward representations such as multivariate distributions or implicit reward functions. These advancements are improving the applicability of RL to complex real-world problems, particularly in robotics, autonomous driving, and human-computer interaction, by enabling more efficient and robust learning.