近似推論による軌道最適化の拡張：探索・共分散制御・適応的リスク
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk
制御を推論問題として捉えるi2cアルゴリズムを解析し、非線形系での開ループ最適解と閉ループ分散低減を両立する専門家線形ガウス制御器、推論定式化に由来する適応的リスク感度、わずかな変更で実現できる共分散制御の3特性を導出した。
著者: Joe Watson, Jan Peters
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
Discrete-time stochastic optimal control remains a challenging problem for general, nonlinear systems under significant uncertainty, with practical solvers typically relying on the certainty equivalence assumption, replanning and/or extensive regularization. Control as inference is an approach that frames stochastic control as an equivalent inference problem, and has demonstrated desirable qualities over existing methods, namely in exploration and regularization. We look specifically at the input inference for control (i2c) algorithm, and derive three key characteristics that enable advanced trajectory optimization: An `expert' linear Gaussian controller that combines the benefits of open-loop optima and closed-loop variance reduction when optimizing for nonlinear systems, inherent adaptive risk sensitivity from the inference formulation, and covariance control functionality with only a minor algorithmic adjustment.
