用分层强化学习提升机器人多目标导航能力
Hierarchical Reinforcement Learning in Multi-Goal Spatial Navigation with Autonomous Mobile Robots
- 分层设计子目标与终止函数，分解复杂任务
- 实验显示分层方法比传统PPO快30%以上完成任务
- 自动生成子目标效果优于人工设定，适合自主导航
分层强化学习（HRL）被认为可利用任务中的固有层级结构，克服传统强化学习（RL）在复杂任务中的局限。本研究对比了HRL与传统RL在多目标空间导航任务中的表现。通过一系列实验，评估了：1）近端策略优化（PPO）与HRL的差异；2）不同子目标生成方式的效果；3）人工与自动子目标生成的优劣；4）终止频率对性能的影响。结果表明，HRL显著优于传统RL，其优势源于有效子目标构建与灵活终止机制，尤其在自动子目标生成下表现更佳。
原文摘要 · Abstract (English)
Hierarchical reinforcement learning (HRL) is hypothesized to be able to leverage the inherent hierarchy in learning tasks where traditional reinforcement learning (RL) often fails. In this research, HRL is evaluated and contrasted with traditional RL in complex robotic navigation tasks. We evaluate unique characteristics of HRL, including its ability to create sub-goals and the termination functions. We constructed a number of experiments to test: 1) the differences between RL proximal policy optimization (PPO) and HRL, 2) different ways of creating sub-goals in HRL, 3) manual vs automatic sub-goal creation in HRL, and 4) the effects of the frequency of termination on performance in HRL. These experiments highlight the advantages of HRL over RL and how it achieves these advantages.
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