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Approximate Dynamic Programming with Gaussian Processes

2008

Conference Paper

ei


In general, it is difficult to determine an optimal closed-loop policy in nonlinear control problems with continuous-valued state and control domains. Hence, approximations are often inevitable. The standard method of discretizing states and controls suffers from the curse of dimensionality and strongly depends on the chosen temporal sampling rate. In this paper, we introduce Gaussian process dynamic programming (GPDP) and determine an approximate globally optimal closed-loop policy. In GPDP, value functions in the Bellman recursion of the dynamic programming algorithm are modeled using Gaussian processes. GPDP returns an optimal statefeedback for a finite set of states. Based on these outcomes, we learn a possibly discontinuous closed-loop policy on the entire state space by switching between two independently trained Gaussian processes. A binary classifier selects one Gaussian process to predict the optimal control signal. We show that GPDP is able to yield an almost optimal solution to an LQ problem using few sample points. Moreover, we successfully apply GPDP to the underpowered pendulum swing up, a complex nonlinear control problem.

Author(s): Deisenroth, MP. and Peters, J. and Rasmussen, CE.
Book Title: ACC 2008
Journal: Proceedings of the 2008 American Control Conference (ACC 2008)
Pages: 4480-4485
Year: 2008
Month: June
Day: 0
Publisher: IEEE Service Center

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Address: Piscataway, NJ, USA
Digital: 0
Event Name: 2008 American Control Conference
Event Place: Seattle, WA, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4975,
  title = {Approximate Dynamic Programming with Gaussian Processes},
  author = {Deisenroth, MP. and Peters, J. and Rasmussen, CE.},
  journal = {Proceedings of the 2008 American Control Conference (ACC 2008)},
  booktitle = {ACC 2008},
  pages = {4480-4485},
  publisher = {IEEE Service Center},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = jun,
  year = {2008},
  month_numeric = {6}
}