時間変動を伴うマルコフ決定過程の到達可能空間の特性評価
Reachable Space Characterization of Markov Decision Processes with Time Variability
時間変動する不確実性を持つマルコフ決定過程に対し、状態の到達時間の平均と分散を解析して到達可能空間を構築し、最適性と計算時間のバランスを取る効率的な反復解法を提案した。海洋データを用いたシミュレーションで有効性を示した。
著者: Junhong Xu, Kai Yin, Lantao Liu
分類: cs.RO, cs.AI
原文アブストラクト
We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in unstructured outdoor environments. We explore the time variability property of the planning stochasticity and investigate the state reachability, based on which we then develop an efficient iterative method that offers a good trade-off between solution optimality and time complexity. The reachability space is constructed by analyzing the means and variances of states' reaching time in the future. We validate our algorithm through extensive simulations using ocean data, and the results show that our method achieves a great performance in terms of both solution quality and computing time.
