Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
This addresses the expensive tuning process for stochastic MPC in control applications, but it is incremental as it extends Bayesian optimisation to handle heteroscedastic noise in this specific context.
The paper tackles the problem of tuning hyper-parameters in stochastic model predictive control, where performance outcomes have input-dependent noise, by proposing a Bayesian optimisation framework that accounts for heteroscedastic noise, with empirical results showing its suitability on benchmark tasks and a physical robot compared to baselines.
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.