Reward Shaping

Reward shaping in reinforcement learning aims to accelerate agent training and improve sample efficiency by modifying the reward function to provide more informative feedback. Current research focuses on developing methods that automatically generate or adapt reward functions, leveraging large language models and incorporating domain knowledge to guide the learning process, often within frameworks like potential-based shaping or by directly shaping Q-values. These advancements are significant because they address the challenge of designing effective reward functions, a critical bottleneck in applying reinforcement learning to complex real-world problems, leading to more efficient and robust agent training across diverse applications.