マルチロボット行動の順序決定のための強化学習フレームワーク
A Reinforcement Learning Framework for Sequencing Multi-Robot Behaviors
複数ロボットの行動を切り替えるタイミングをQ学習で最適化し、各行動の制御パラメータをオンライン勾配降下法で調整する手法を提案。護衛と物体操作のミッションで有効性を実機検証した。
著者: Pietro Pierpaoli, Thinh T. Doan, Justin Romberg, Magnus Egerstedt
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Given a list of behaviors and associated parameterized controllers for solving different individual tasks, we study the problem of selecting an optimal sequence of coordinated behaviors in multi-robot systems for completing a given mission, which could not be handled by any single behavior. In addition, we are interested in the case where partial information of the underlying mission is unknown, therefore, the robots must cooperatively learn this information through their course of actions. Such problem can be formulated as an optimal decision problem in multi-robot systems, however, it is in general intractable due to modeling imperfections and the curse of dimensionality of the decision variables. To circumvent these issues, we first consider an alternate formulation of the original problem through introducing a sequence of behaviors' switching times. Our main contribution is then to propose a novel reinforcement learning based method, that combines Q-learning and online gradient descent, for solving this reformulated problem. In particular, the optimal sequence of the robots' behaviors is found by using Q-learning while the optimal parameters of the associated controllers are obtained through an online gradient descent method. Finally, to illustrate the effectiveness of our proposed method we implement it on a team of differential-drive robots for solving two different missions, namely, convoy protection and object manipulation.
