Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning

Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards. Drawing inspiration from this Toddler-Inspired Reward Transition, we set out to explore the implications of varying reward transitions when incorporated into Reinforcement Learning (RL) tasks. Central to our inquiry is the transition from sparse to potential-based dense rewards, which share optimal strategies regardless of reward changes. Through various experiments, including those in egocentric navigation and robotic arm manipulation tasks, we found that proper reward transitions significantly influence sample efficiency and success rates. Of particular note is the efficacy of the toddler-inspired Sparse-to-Dense (S2D) transition. Beyond these performance metrics, using Cross-Density Visualizer technique, we observed that transitions, especially the S2D, smooth the policy loss landscape, promoting wide minima that enhance generalization in RL models.

Authors

• Junseok Park Seoul National University
• Yoonsung Kim Seoul National University
• Hee bin Yoo Seoul National University
• Min Whoo Lee Seoul National University
• Kibeom Kim Seoul National University
• Won-Seok Choi Seoul National University
• Minsu Lee School of AI Convergence, Sungshin Women’s University
• Byoung-Tak Zhang Interdisciplinary Program in Artificial Intelligence, Seoul National University AIIS, Seoul National University Department of Computer Science, Seoul National University

Keywords

• CMS: Simulating Human Behavior
• HAI: Understanding People, Theories, Concepts and Methods
• ML: Bio-inspired Learning