Bayesian Learning Approach to Model Predictive Control
This work addresses a theoretical gap for researchers in stochastic optimal control, but it is incremental as it builds on existing frameworks without introducing a new paradigm.
The study tackled the lack of connections between Bayesian learning and model predictive control frameworks by integrating a Bayesian learning perspective into model predictive control, resulting in a streamlined explanation of design choices for algorithm diversification.
This study presents a Bayesian learning perspective towards model predictive control algorithms. High-level frameworks have been developed separately in the earlier studies on Bayesian learning and sampling-based model predictive control. On one hand, the Bayesian learning rule provides a general framework capable of generating various machine learning algorithms as special instances. On the other hand, the dynamic mirror descent model predictive control framework is capable of diversifying sample-rollout-based control algorithms. However, connections between the two frameworks have still not been fully appreciated in the context of stochastic optimal control. This study combines the Bayesian learning rule point of view into the model predictive control setting by taking inspirations from the view of understanding model predictive controller as an online learner. The selection of posterior class and natural gradient approximation for the variational formulation governs diversification of model predictive control algorithms in the Bayesian learning approach to model predictive control. This alternative viewpoint complements the dynamic mirror descent framework through streamlining the explanation of design choices.