Probabilistic differential dynamic programming

Advances in Neural Information Processing Systems

Volumn 3, Issue January, 2014, Pages 1907-1915

  Pan, Yunpeng ^a   Theodorou, Evangelos A ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION SCIENCE; TIME VARYING CONTROL SYSTEMS;

DIFFERENTIAL DYNAMIC PROGRAMMING; EFFECTIVENESS AND EFFICIENCIES; GAUSSIAN PROCESSES; LOCAL APPROXIMATION; NOMINAL TRAJECTORY; NON-TRIVIAL TASKS; STATE OF THE ART; TRAJECTORY OPTIMIZATION;

DYNAMIC PROGRAMMING;

EID: 84937825341     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. D.H. Jacobson and D.Q. Mayne. Differential dynamic programming. Elsevier Sci. Publ., 1970.
2. E. Todorov and W. Li. A generalized iterative lqg method for locally-optimal feedback control of constrained nonlinear stochastic systems. In American Control Conference, pages 300-306, June 2005.
3. Y. Tassa, T. Erez, and W. D. Smart. Receding horizon differential dynamic programming. In NIPS, pages 1465-1472.
4. E. Theodorou, Y. Tassa, and E. Todorov. Stochastic differential dynamic programming. In American Control Conference, pages 1125-1132, June 2010.
5. D. Mitrovic, S. Klanke, and S. Vijayakumar. Adaptive optimal feedback control with learned internal dynamics models. In From Motor Learning to Interaction Learning in Robots, pages 65-84. Springer, 2010.
6. J. Van Den Berg, S. Patil, and R. Alterovitz. Motion planning under uncertainty using iterative local optimization in belief space. The International Journal of Robotics Research, 31(11):1263-1278, 2012.
7. S. Levine and V. Koltun. Variational policy search via trajectory optimization. In NIPS, pages 207-215. 2013.
8. J. Morimoto and C.G. Atkeson. Minimax differential dynamic programming: An application to robust biped walking. In NIPS, pages 1539-1546, 2002.
9. P. Abbeel, A. Coates, M. Quigley, and A. Y. Ng. An application of reinforcement learning to aerobatic helicopter flight. In NIPS, pages 1-8, 2007.
10. C. E. Rasmussen and M. Kuss. Gaussian processes in reinforcement learning. In NIPS, pages 751-759, 2003.
11. D. Nguyen-Tuong, J. Peters, and M. Seeger. Local gaussian process regression for real time online model learning. In NIPS, pages 1193-1200, 2008.
12. M. P. Deisenroth, C. E. Rasmussen, and J. Peters. Gaussian process dynamic programming. Neurocomputing, 72(7):1508-1524, 2009.
13. M. P. Deisenroth and C. E. Rasmussen. Pilco: A model-based and data-efficient approach to policy search. In ICML, pages 465-472, 2011.
14. M. P. Deisenroth, D. Fox, and C. E. Rasmussen. Gaussian processes for data-efficient learning in robotics and control. IEEE Transsactions on Pattern Analysis and Machine Intelligence, 27:75-90, 2014.
15. P. Hemakumara and S. Sukkarieh. Learning uav stability and control derivatives using gaussian processes. IEEE Transactions on Robotics, 29:813-824, 2013.
16. J. Quinonero Candela, A. Girard, J. Larsen, and C. E. Rasmussen. Propagation of uncertainty in bayesian kernel models-application to multiple-step ahead forecasting. In IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003.
17. C.K.I. Williams and C.E. Rasmussen. Gaussian processes for machine learning. MIT Press, 2006.
18. L. Csató and M. Opper. Sparse online gaussian processes. Neural Computation, 14(3):641-668, 2002.
19. E. Snelson and Z. Ghahramani. Sparse gaussian processes using pseudo-inputs. In NIPS, pages 1257-1264, 2005.
20. W. Zhong and H. Rock. Energy and passivity based control of the double inverted pendulum on a cart. In International Conference on Control Applications, pages 896-901, Sept 2001.
21. T. Raiko and M. Tornio. Variational bayesian learning of nonlinear hidden state-space models for model predictive control. Neurocomputing, 72(16):3704-3712, 2009.
22. H. van Hasselt. Insights in reinforcement learning. Hado van Hasselt, 2011.