多様な確率的計画：複数の計画は一つより優れる
Multiple Plans are Better than One: Diverse Stochastic Planning
人間の選好など完全にはモデル化できない目的を持つ計画問題に対し、既知の目的関数の下で多様かつ準最適な方策群を生成する「多様確率的計画」を定式化し、Frank-Wolfe法に基づく解法を提案した論文。
著者: Mahsa Ghasemi, Evan Scope Crafts, Bo Zhao, Ufuk Topcu
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
In planning problems, it is often challenging to fully model the desired specifications. In particular, in human-robot interaction, such difficulty may arise due to human's preferences that are either private or complex to model. Consequently, the resulting objective function can only partially capture the specifications and optimizing that may lead to poor performance with respect to the true specifications. Motivated by this challenge, we formulate a problem, called diverse stochastic planning, that aims to generate a set of representative -- small and diverse -- behaviors that are near-optimal with respect to the known objective. In particular, the problem aims to compute a set of diverse and near-optimal policies for systems modeled by a Markov decision process. We cast the problem as a constrained nonlinear optimization for which we propose a solution relying on the Frank-Wolfe method. We then prove that the proposed solution converges to a stationary point and demonstrate its efficacy in several planning problems.
