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Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems

arxiv.org · 40,397 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. HTML conversions sometimes display errors due to content that did not convert correctly from the source. This paper uses the following packages that are not yet supported by the HTML conversion tool. Feedback on these issues are not necessary; they are known and are being worked on. Authors: achieve the best HTML results from your LaTeX submissions by following these best practices. Abstract: The ability of a robot to plan complex behaviors with real-time computation, rather than adhering to predesigned or offline-learned routines, alleviates the need for specialized algorithms or training for each problem instance. Monte Carlo Tree Search is a powerful planning algorithm that strategically explores simulated future possibilities, but it requires a discr

Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems Benjamin Riviere ∗1 , John Lathrop ∗1 , Soon-Jo Chung 1 ∗ The first two authors contributed equally to this article. 1 Department of Engineering and Applied Science, California Institute of Technology This is the accepted version of Science Robotics Vol 9, Issue 97 DOI: 10.1126/scirobotics.ado101, Link to paper , Link to video , Link to code Abstract: The ability of a robot to plan complex behaviors with real-time computation, r

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