Reward Function Design

Reward function design in reinforcement learning (RL) focuses on creating effective reward signals that guide agents towards desired behaviors, a crucial yet challenging aspect of training intelligent systems. Current research emphasizes automating this process, leveraging large language models (LLMs) to generate and refine reward functions from natural language descriptions or demonstrations, often incorporating techniques like reward shaping and iterative refinement. This work is significant because effective reward design is critical for the successful application of RL in diverse fields, including robotics, autonomous driving, and drug discovery, enabling more efficient and robust training of complex systems.