Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian Optimisationcontinuous-controlContinuous ControlmodelModel Predictive ControlSimilar Papers 제목 키워드 기반
Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty
We propose an adaptive optimisation approach for tuning stochastic model predictive control (MPC) hyper-parameters while jointly estimating probability distributions of the transition model parameters based on performanc…
Bayesian OptimisationmodelModel Predictive ControlAdaptive Model Predictive Control by Learning Classifiers
Stochastic model predictive control has been a successful and robust control framework for many robotics tasks where the system dynamics model is slightly inaccurate or in the presence of environment disturbances. Despit…
Bayesian OptimisationDensity Ratio EstimationmodelModel Predictive ControlAchieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs. A frequently-overlooked modelling consideration in Bayesian optimisation str…
Bayesian OptimisationDecision MakingDrug DiscoveryAntifragile and Robust Heteroscedastic Bayesian Optimisation
Bayesian Optimisation is an important decision-making tool for high-stakes applications in drug discovery and materials design. An oft-overlooked modelling consideration however is the representation of input-depende…
Bayesian OptimisationDecision MakingDrug DiscoveryBayesian Quantile and Expectile Optimisation
Bayesian optimisation (BO) is widely used to optimise stochastic black box functions. While most BO approaches focus on optimising conditional expectations, many applications require risk-averse strategies and alternativ…
Bayesian OptimisationGaussian ProcessesThompson Sampling