  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.
