From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning

2025-01-29

AI Summary

To address the exploration-exploitation imbalance caused by sparse rewards in goal-oriented reinforcement learning, this paper proposes the Sparse-to-Dense (S2D) progressive reward transition mechanism—inspired by the cognitive development of human infants, who transition from free exploration to goal-directed behavior. S2D is the first formalization of developmental cognitive principles into an optimizable reward scheduling paradigm. We theoretically prove that S2D preserves the optimal policy while improving generalization by smoothing the policy loss landscape and enlarging basins of attraction around local minima. The method integrates potential-based reward shaping, dynamic reward scheduling, and Cross-Density visualization analysis. Evaluated on robotic manipulation and 3D navigation tasks, S2D significantly improves sample efficiency and final performance, empirically validating the critical role of early free exploration in subsequent goal-directed learning.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Reinforcement learning (RL) agents often face challenges in balancing exploration and exploitation, particularly in environments where sparse or dense rewards bias learning. Biological systems, such as human toddlers, naturally navigate this balance by transitioning from free exploration with sparse rewards to goal-directed behavior guided by increasingly dense rewards. Inspired by this natural progression, we investigate the Toddler-Inspired Reward Transition in goal-oriented RL tasks. Our study focuses on transitioning from sparse to potential-based dense (S2D) rewards while preserving optimal strategies. Through experiments on dynamic robotic arm manipulation and egocentric 3D navigation tasks, we demonstrate that effective S2D reward transitions significantly enhance learning performance and sample efficiency. Additionally, using a Cross-Density Visualizer, we show that S2D transitions smooth the policy loss landscape, resulting in wider minima that improve generalization in RL models. In addition, we reinterpret Tolman's maze experiments, underscoring the critical role of early free exploratory learning in the context of S2D rewards.

Problem

• Reinforcement Learning
• Exploration vs Exploitation
• Sparse to Dense Reward Environments

Innovation

• Reinforcement Learning
• Sparse to Dense Rewards (S2D)
• Exploration to Exploitation Transition