Path integral policy improvement with covariance matrix adaptation

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 1, Issue , 2012, Pages 281-288

  Stulp, Freek ^a,b   Sigaud, Olivier ^c  

^a ENSTA PARISTECH   (France)

^b INRIA   (France)

^c UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CONCEPTUAL LEVELS; CONTINUOUS STATE; COVARIANCE MATRIX ADAPTATION; CROSS-ENTROPY METHOD; NOVEL ALGORITHM; PARAMETERIZED; PATH INTEGRAL; STATISTICAL ESTIMATION; STOCHASTIC OPTIMAL CONTROL;

ALGORITHMS; OPTIMIZATION; QUANTUM THEORY; REINFORCEMENT LEARNING;

ITERATIVE METHODS;

EID: 84867129779     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Busoniu, L., Ernst, D., Schutter, B. De, and Babuska, R. Cross-entropy optimization of control policies with adaptive basis functions. IEEE Transactions on Systems, Man, and Cybernetics, 41(1):196-209, 2011.
2. Dattorro, J. Convex Optimization & Euclidean Distance Geometry. Meboo Publishing USA, 2011.
3. Hansen, N. and Ostermeier, A. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2):159-195, 2001.
4. Heidrich-Meisnerm V. and Igel, C. Evolution strategies for direct policy search. In Proc. of the Int'l Conference on Parallel Problem Solving from Nature, 2008.
5. Ijspeert, A. J., Nakanishi, J., and Schaal, S. Movement imitation with nonlinear dynamical systems in humanoid robots. In Proc. of the IEEE Int'l Conference on Robotics and Automation (ICRA), 2002.
6. Kober, J. and Peters, J. Policy search for motor primitives in robotics. Machine Learning, 84:171-203, 2011.
7. Kobilarov, M. Cross-entropy randomized motion planning. In Proceedings of Robotics: Science and Systems, 2011.
8. Mannor, S., Rubinstein, R. Y., and Gat, Y. The Cross-Entropy Method for fast policy search. In Proceedings of the Int'l Conference on Machine Learning, 2003.
9. Marin, D., Decock, J., Rigoux, L., and Sigaud, O. Learning cost-efficient control policies with XCSF: Generalization capabilities and further improvement. In Proc. of Genetic and evolutionary computation, 2011.
10. Peters, J. and Schaal, S.. Natural actor-critic. Neurocomputing, 71(7-9):1180-1190, 2008.
11. Rückstiess, T., Sehnke, F., Schaul, T., Wierstra, D., Sun, Y., and Schmidhuber, J.. Exploring parameter space in reinforcement learning. Paladyn. Journal of Behavioral Robotics, 1:14-24, 2010.
12. Stulp, F., Theodorou, E., Buchli, J., and Schaal, S. Learning to grasp under uncertainty. In Proceedings of the Int'l Conference on Robotics and Automation, 2011.
13. Theodorou, E., Buchli, J., and Schaal, S.. A generalized path integral control approach to reinforcement learning. J. of Machine Learning Research, 11:3137-3181, 2010.
14. Williams, R. J. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8:229-256, 1992.