模仿幼儿学习过程，让机器人从探索到目标导向更高效地训练。
From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning
- 先稀疏奖励自由探索，再逐步过渡到密集奖励引导目标行为。
- 在机械臂和3D导航任务中，样本效率提升40%以上，性能显著改善。
- 揭示早期探索对长期学习的关键作用，适合强化学习初学者参考。
强化学习代理在稀疏或密集奖励环境下常面临探索与利用的平衡难题。受人类幼儿自然发展过程启发，我们研究了从自由探索（稀疏奖励）向目标导向行为（基于势能的密集奖励）的奖励过渡机制。通过在动态机械臂操作和自我中心3D导航任务上的实验，我们证明有效的稀疏到密集奖励过渡（S2D）能显著提升学习性能与样本效率。此外，借助跨密度可视化工具，我们发现该过渡可平滑策略损失曲面，形成更宽的极小值，增强模型泛化能力。我们还重新诠释了托尔曼迷宫实验，强调早期自由探索在S2D框架下的关键作用。
原文摘要 · Abstract (English)
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.
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