Reward Engineering

Reward engineering, the process of designing reward functions for reinforcement learning (RL) agents, aims to efficiently guide agents towards desired behaviors. Current research focuses on mitigating the need for extensive manual reward design through methods like preference-based RL, which leverages human feedback or large language models (LLMs) to implicitly define rewards, and techniques that learn reusable or adaptable reward functions across multiple tasks. These advancements are significant because they reduce the substantial human effort and domain expertise traditionally required for effective RL, enabling broader application in robotics, game AI, and other complex domains.