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

TL;DR

This work investigates how reward density transitions influence learning in goal-oriented RL by proposing a Toddler-Inspired Sparse to Potential-based Dense (S2D) curriculum. The authors formalize S2D within a curriculum-learning framework and use a Cross-Density Visualizer to analyze post-transition 3D policy loss landscapes, observing that S2D smooths the loss surface and promotes wide minima. Empirical results across varied tasks—including LunarLander, CartPole-Reacher, UR5-Reacher, and ViZDoom generalization scenarios—show improved sample efficiency, higher success rates, and better generalization compared with sparse, dense, and other intrinsic-motivation baselines. The findings suggest that biologically inspired, density-varying reward structures can enhance exploration-exploitation balance and generalization in RL, though automatic optimization of transition timing remains an open avenue for future work.

Abstract

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.

Figures (18)

• Figure 1: Parallel learning trajectories: toddlers and agents. (a) The figure compares a toddler's learning journey with an agent's. On the left, a toddler freely explores the apple, symbolizing sparse reward learning. As we transition right, the toddler's focus on specific tasks reflects goal-guided learning. Similarly, the agent's progression from sparse to potential-based dense rewards is charted above, highlighting parallels in learning evolution. (b) As reward transitions occur, the depth of local minima reduces, leading to a wide minima via the smoothing effect, thereby enhancing more generalization.
• Figure 2: Overview of the baseline rewards. The S2D presents reward inspired by toddler learning. In sparse rewards, agents are rewarded upon reaching the target. For potential-based dense rewards, they get an extra reward determined by the distance to a specific unit from the object.
• Figure 3: Experimental environments. In particular, (a) and (b) are environments for evaluating generalization.
• Figure 4: Performance of the agent with various reward types in multiple goal-oriented tasks. Notably, in (c) LunarLander, the accumulated reward from intrinsic rewards was significantly below zero, indicated by a dashed line.
• Figure 5: Generalization performance of the ViZDoom agent with various types of rewards.

Theorems & Definitions (2)

• Definition 1: Curriculum
• Definition 2: Toddler-inspired S2D-curriculum