Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning
This work addresses sample efficiency and generalization challenges in reinforcement learning for tasks like navigation and manipulation, though it appears incremental by adapting known reward shaping techniques.
The paper tackles the problem of improving sample efficiency and success rates in reinforcement learning by exploring reward transitions from sparse to dense rewards, inspired by toddler learning. The results show that the toddler-inspired Sparse-to-Dense transition significantly enhances performance, with observed improvements in generalization through smoothed policy loss landscapes.
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.