未知環境における線形時相論理目標のためのモデルフリー強化学習
Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives
遷移確率やモデル構造が未知の確率的ゲームにおいて、LTL仕様を満たす確率を最大化する制御戦略をモデルフリー強化学習で学習する手法を提案し、運動計画の事例で有効性を示した。
著者: Alper Kamil Bozkurt, Yu Wang, Michael Zavlanos, Miroslav Pajic
分類: cs.RO, cs.LO
原文アブストラクト
We study the problem of synthesizing control strategies for Linear Temporal Logic (LTL) objectives in unknown environments. We model this problem as a turn-based zero-sum stochastic game between the controller and the environment, where the transition probabilities and the model topology are fully unknown. The winning condition for the controller in this game is the satisfaction of the given LTL specification, which can be captured by the acceptance condition of a deterministic Rabin automaton (DRA) directly derived from the LTL specification. We introduce a model-free reinforcement learning (RL) methodology to find a strategy that maximizes the probability of satisfying a given LTL specification when the Rabin condition of the derived DRA has a single accepting pair. We then generalize this approach to LTL formulas for which the Rabin condition has a larger number of accepting pairs, providing a lower bound on the satisfaction probability. Finally, we illustrate applicability of our RL method on two motion planning case studies.
