Ada-NAV: Adaptive Trajectory Length-Based Sample Efficient Policy Learning for Robotic Navigation

arXiv:2306.06192v4 Announce Type: replace-cross
Abstract: Trajectory length stands as a crucial hyperparameter within reinforcement learning (RL) algorithms, significantly contributing to the sample inefficiency in robotics applications. Motivated by the pivotal role trajectory length plays in the training process, we introduce Ada-NAV, a novel adaptive trajectory length scheme designed to enhance the training sample efficiency of RL algorithms in robotic navigation tasks. Unlike traditional approaches that treat trajectory length as a fixed hyperparameter, we propose to dynamically adjust it based on …

abstract ada algorithms applications arxiv cs.ai cs.lg cs.ro hyperparameter navigation novel pivotal policy process reinforcement reinforcement learning robotic robotics role sample training trajectory type