2019/02/28 by Binghong Chen, Bo Dai, Chen, Binghong +9 · 4 citations
Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Multimodal Machine Learning Applications #Robotic Path Planning Algorithms #Robotics (cs.RO) #cs.LG #cs.RO #stat.ML
paper · pdf · doi:10.48550/arxiv.1903.00070
26 pages, 74 figures, ICLR 2020 spotlight
openalex publication_date 2019/02/28 · openalex created_date 2019/12/26 · arxiv created 2020/02/23 · arxiv updated 2020/02/25 · openalex updated_date 2026/07/28
We propose a meta path planning algorithm named Neural Exploration-Exploitation Trees~(NEXT) for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces. Compared to more classical sampling-based methods like RRT, our approach achieves much better sample efficiency in high-dimensions and can benefit from prior experience of planning in similar environments. More specifically, NEXT exploits a novel neural architecture which can learn promising search directions from problem structures. The learned prior is then integrated into a UCB-type algorithm to achieve an online balance between exploration and exploitation when solving a new problem. We conduct thorough experiments to show that NEXT accomplishes new planning problems with more compact search trees and significantly outperforms state-of-the-art methods on several benchmarks.