Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Learning Approach to Model Predictive Control   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.

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The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org