  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.
