Learning neural network policies with guided policy search under unknown dynamics

Advances in Neural Information Processing Systems

Volumn 2, Issue January, 2014, Pages 1071-1079

  Levine, Sergey ^a   Abbeel, Pieter ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; DYNAMICS; INFORMATION SCIENCE;

COMPLEX NEURAL NETWORKS; CONTACT DISCONTINUITIES; CONTINUOUS PROBLEMS; LEARNING NEURAL NETWORKS; LOCAL LINEAR MODELS; MODEL BASED TECHNIQUES; NON-SMOOTH DYNAMICS; ROBOTIC MANIPULATION;

ITERATIVE METHODS;

EID: 84937822296     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. J. A. Bagnell and J. Schneider. Covariant policy search. In International Joint Conference on Artificial Intelligence (IJCAI), 2003.
2. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, New York, NY, 2004.
3. A. Coates, P. Abbeel, and A. Ng. Learning for control from multiple demonstrations. In International Conference on Machine Learning (ICML), 2008.
4. M. Deisenroth, G. Neumann, and J. Peters. A survey on policy search for robotics. Foundations and Trends in Robotics, 2(1-2): 1-142, 2013.
5. M. Deisenroth and C. Rasmussen. PILCO: a model-based and data-efficient approach to policy search. In International Conference on Machine Learning (ICML), 2011.
6. Y. Engel, P. Szabó, and D. Volkinshtein. Learning to control an octopus arm with Gaussian process temporal difference methods. In Advances in Neural Information Processing Systems (NIPS), 2005.
7. R. Fletcher. Practical Methods of Optimization. Wiley-Interscience, New York, NY, 1987.
8. A. Ijspeert, J. Nakanishi, and S. Schaal. Learning attractor landscapes for learning motor primitives. In Advances in Neural Information Processing Systems (NIPS), 2003.
9. S. M. Khansari-Zadeh and A. Billard. BM: An iterative algorithm to learn stable non-linear dynamical systems with gaussian mixture models. In International Conference on Robotics and Automation (ICRA), 2010.
10. J. Kober, J. A. Bagnell, and J. Peters. Reinforcement learning in robotics: A survey. International Journal of Robotic Research, 32(11): 1238-1274, 2013.
11. J. Kober and J. Peters. Learning motor primitives for robotics. In International Conference on Robotics and Automation (ICRA), 2009.
12. S. Levine and V. Koltun. Guided policy search. In International Conference on Machine Learning (ICML), 2013.
13. S. Levine and V. Koltun. Variational policy search via trajectory optimization. In Advances in Neural Information Processing Systems (NIPS), 2013.
14. S. Levine and V. Koltun. Learning complex neural network policies with trajectory optimization. In International Conference on Machine Learning (ICML), 2014.
15. W. Li and E. Todorov. Iterative linear quadratic regulator design for nonlinear biological movement systems. In ICINCO (1), pages 222-229, 2004.
16. R. Lioutikov, A. Paraschos, G. Neumann, and J. Peters. Sample-based information-theoretic stochastic optimal control. In International Conference on Robotics and Automation, 2014.
17. J. Peters, K. Mülling, and Y. Altün. Relative entropy policy search. In AAAI Conference on Artificial Intelligence, 2010.
18. J. Peters and S. Schaal. Applying the episodic natural actor-critic architecture to motor primitive learning. In European Symposium on Artificial Neural Networks (ESANN), 2007.
19. J. Peters and S. Schaal. Reinforcement learning of motor skills with policy gradients. Neural Networks, 21(4): 682-697, 2008.
20. K. Rawlik, M. Toussaint, and S. Vijayakumar. On stochastic optimal control and reinforcement learning by approximate inference. In Robotics: Science and Systems, 2012.
21. R. Rubinstein and D. Kroese. The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. Springer, 2004.
22. F. Stulp and O. Sigaud. Path integral policy improvement with covariance matrix adaptation. In International Conference on Machine Learning (ICML), 2012.
23. Y. Tassa, T. Erez, and E. Todorov. Synthesis and stabilization of complex behaviors through online trajectory optimization. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012.
24. E. Theodorou, J. Buchli, and S. Schaal. Reinforcement learning of motor skills in high dimensions. In International Conference on Robotics and Automation (ICRA), 2010.
25. M. Toussaint. Robot trajectory optimization using approximate inference. In International Conference on Machine Learning (ICML), 2009.