Markov Chain Monte Carlo for Koopman-based Optimal Control: Technical Report
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916317046505472 |
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| author | Hespanha, João Camsari, Kerem |
| author_facet | Hespanha, João Camsari, Kerem |
| contents | We propose a Markov Chain Monte Carlo (MCMC) algorithm based on Gibbs sampling with parallel tempering to solve nonlinear optimal control problems. The algorithm is applicable to nonlinear systems with dynamics that can be approximately represented by a finite dimensional Koopman model, potentially with high dimension. This algorithm exploits linearity of the Koopman representation to achieve significant computational saving for large lifted states. We use a video-game to illustrate the use of the method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01788 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Markov Chain Monte Carlo for Koopman-based Optimal Control: Technical Report Hespanha, João Camsari, Kerem Optimization and Control We propose a Markov Chain Monte Carlo (MCMC) algorithm based on Gibbs sampling with parallel tempering to solve nonlinear optimal control problems. The algorithm is applicable to nonlinear systems with dynamics that can be approximately represented by a finite dimensional Koopman model, potentially with high dimension. This algorithm exploits linearity of the Koopman representation to achieve significant computational saving for large lifted states. We use a video-game to illustrate the use of the method. |
| title | Markov Chain Monte Carlo for Koopman-based Optimal Control: Technical Report |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2405.01788 |