方策分解の探索手法：遺伝的アルゴリズムとモンテカルロ木探索による組合せ課題の緩和
Search Methods for Policy Decompositions
複雑な制御問題を低次元の部分問題に分解する「方策分解」において、遺伝的アルゴリズムとモンテカルロ木探索を用いて分解戦略の組合せ爆発を緩和し、マニピュレータの振り上げ・二足歩行のバランス・クアッドコプターのホバリング制御で有効性を示した論文。
著者: Ashwin Khadke, Hartmut Geyer
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Computing optimal control policies for complex dynamical systems requires approximation methods to remain computationally tractable. Several approximation methods have been developed to tackle this problem. However, these methods do not reason about the suboptimality induced in the resulting control policies due to these approximations. We introduced Policy Decomposition, an approximation method that provides a suboptimality estimate, in our earlier work. Policy decomposition proposes strategies to break an optimal control problem into lower-dimensional subproblems, whose optimal solutions are combined to build a control policy for the original system. However, the number of possible strategies to decompose a system scale quickly with the complexity of a system, posing a combinatorial challenge. In this work we investigate the use of Genetic Algorithm and Monte-Carlo Tree Search to alleviate this challenge. We identify decompositions for swing-up control of a 4 degree-of-freedom manipulator, balance control of a simplified biped, and hover control of a quadcopter.
