Deep Reinforcement Learning (DRL) has emerged as a promising approach to enhancing motion control and decision-making through a wide range of robotic applications. While prior research has demonstrated the efficacy of DRL algorithms in facilitating autonomous mapless navigation for aerial and terrestrial mobile robots, these methods often grapple with poor generalization when faced with unknown tasks and environments. This paper explores the impact of the Delayed Policy Updates (DPU) technique on fostering generalization to new situations, and bolstering the overall performance of agents. Our analysis of DPU in aerial and terrestrial mobile robots reveals that this technique significantly curtails the lack of generalization and accelerates the learning process for agents, enhancing their efficiency across diverse tasks and unknown scenarios.

翻译：深度强化学习（DRL）已成为一种通过广泛机器人应用来增强运动控制与决策能力的前沿方法。尽管先前研究已证明DRL算法在实现空中与地面移动机器人自主无地图导航方面的有效性，但这些方法在面对未知任务和环境时常常受限于较差的泛化性能。本文探讨了延迟策略更新（DPU）技术对促进新情境泛化能力及增强智能体整体性能的影响。我们对DPU在空中与地面移动机器人中的应用分析表明，该技术能显著缓解泛化能力不足的问题，加速智能体的学习进程，从而提升其在多样化任务与未知场景中的运行效率。