Computer Science > Machine Learning
arXiv:2203.02720 (cs)
[Submitted on 5 Mar 2022 (v1), last revised 11 Mar 2022 (this version, v2)]
Title:Bayesian Learning Approach to Model Predictive Control
View a PDF of the paper titled Bayesian Learning Approach to Model Predictive Control, by Namhoon Cho and 3 other authors
View PDFAbstract: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.
| Comments: | 6 pages, IEEE L-CSS with CDC |
| Subjects: | Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY); Optimization and Control (math.OC) |
| Cite as: | arXiv:2203.02720 [cs.LG] |
| (orarXiv:2203.02720v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2203.02720 arXiv-issued DOI via DataCite |
Submission history
From: Namhoon Cho [view email][v1] Sat, 5 Mar 2022 12:15:03 UTC (16 KB)
[v2] Fri, 11 Mar 2022 12:41:27 UTC (17 KB)
Full-text links:
Access Paper:
- View PDF
- TeX Source
- Other Formats
View a PDF of the paper titled Bayesian Learning Approach to Model Predictive Control, by Namhoon Cho and 3 other authors
Current browse context:
cs.LG
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
IArxiv Recommender(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.