確率的モデル予測制御のための異分散ベイズ最適化
Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
確率的MPCのハイパーパラメータ調整において、入力依存で変化するノイズ（異分散性）を考慮したベイズ最適化フレームワークを提案し、連続制御ベンチマークと実機ロボットで有効性を示した。
著者: Rel Guzman, Rafael Oliveira, Fabio Ramos
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
Model predictive control (MPC) has been successful in applications involving the control of complex physical systems. This class of controllers leverages the information provided by an approximate model of the system's dynamics to simulate the effect of control actions. MPC methods also present a few hyper-parameters which may require a relatively expensive tuning process by demanding interactions with the physical system. Therefore, we investigate fine-tuning MPC methods in the context of stochastic MPC, which presents extra challenges due to the randomness of the controller's actions. In these scenarios, performance outcomes present noise, which is not homogeneous across the domain of possible hyper-parameter settings, but which varies in an input-dependent way. To address these issues, we propose a Bayesian optimisation framework that accounts for heteroscedastic noise to tune hyper-parameters in control problems. Empirical results on benchmark continuous control tasks and a physical robot support the proposed framework's suitability relative to baselines, which do not take heteroscedasticity into account.
