Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning
This addresses generalization challenges for robotic control applications, though it appears incremental as it builds on existing DRL methods with a known technique.
The paper tackles the problem of poor generalization in deep reinforcement learning for aerial and terrestrial mobile robots when facing unknown tasks and environments, finding that the Delayed Policy Updates technique significantly reduces this issue and accelerates learning.
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.