Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning

TL;DR

This paper investigates how Delayed Policy Updates improve the generalization and learning efficiency of deep reinforcement learning agents in aerial and terrestrial mobile robots across diverse and unknown environments.

Contribution

It introduces the application of Delayed Policy Updates to enhance generalization and accelerate learning in mobile robot control using DRL.

Findings

DPU significantly improves generalization to new environments.

DPU accelerates the learning process for mobile robots.

Agents with DPU perform better across diverse tasks.

Abstract

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.