Reward Transition

Reward transition in reinforcement learning (RL) focuses on how the reward signal changes over time and its impact on agent learning. Current research investigates optimal reward structures, such as transitioning from sparse to dense rewards inspired by human development, and the use of intrinsic motivation methods like curiosity to guide exploration in reward-sparse environments. These studies aim to improve sample efficiency, generalization, and the ability to train RL agents in challenging scenarios with limited or delayed feedback, ultimately advancing the applicability of RL to real-world problems. The findings are relevant to various fields, including robotics and AI safety, where efficient and robust learning from limited data is crucial.