継続的エリアスイープのための深層Rラーニング
Deep R-Learning for Continual Area Sweeping
イベント分布が未知の環境で、関心イベントの検出率を最大化する継続的エリアスイープ問題に対し、半マルコフ決定過程に基づく強化学習手法を提案し、既存の貪欲法より大幅な改善を示した。
著者: Rishi Shah, Yuqian Jiang, Justin Hart, Peter Stone
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
Coverage path planning is a well-studied problem in robotics in which a robot must plan a path that passes through every point in a given area repeatedly, usually with a uniform frequency. To address the scenario in which some points need to be visited more frequently than others, this problem has been extended to non-uniform coverage planning. This paper considers the variant of non-uniform coverage in which the robot does not know the distribution of relevant events beforehand and must nevertheless learn to maximize the rate of detecting events of interest. This continual area sweeping problem has been previously formalized in a way that makes strong assumptions about the environment, and to date only a greedy approach has been proposed. We generalize the continual area sweeping formulation to include fewer environmental constraints, and propose a novel approach based on reinforcement learning in a Semi-Markov Decision Process. This approach is evaluated in an abstract simulation and in a high fidelity Gazebo simulation. These evaluations show significant improvement upon the existing approach in general settings, which is especially relevant in the growing area of service robotics.
