Fitted Q-iteration by advantage weighted regression

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 1177-1184

  Neumann, Gerhard ^a   Peters, Jan ^b  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

ACTION SELECTION; ACTION SPACES; CONTINUOUS ACTIONS; GREEDY ACTION; HIGH QUALITY; LEARNING PROCESS; OPTIMISATIONS; REAL-WORLD TASK; TECHNICAL APPLICATIONS; WEIGHTED REGRESSION;

ITERATIVE METHODS;

EID: 70049104729     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. R. Sutton and A. Barto, Reinforcement Learning. Boston, MA: MIT Press, 1998.
2. J. A. Boyan and A.W. Moore, "Generalization in reinforcement learning: Safely approximating the value function," in Advances in Neural Information Processing Systems 7, pp. 369-376, MIT Press, 1995.
3. P. Viviani and T. Flash, "Minimum-jerk, two-thirds power law, and isochrony: Converging approaches to movement planning," Journal of Experimental Psychology: Human Perception and Performance, vol. 21, no. 1, pp. 32-53, 1995.
4. R. M. Alexander, "A minimum energy cost hypothesis for human arm trajectories," Biological Cybernetics, vol. 76, pp. 97-105, 1997. (Pubitemid 127665170)
5. C. M. Harris and D. M. Wolpert, "Signal-dependent noise determines motor planning.," Nature, vol. 394, pp. 780-784, August 1998. (Pubitemid 28391641)
6. M. Riedmiller, "Neural fitted Q-iteration - first experiences with a data efficient neural reinforcement learning method," in Proceedings of the European Conference on Machine Learning (ECML), 2005.
7. R. Sutton, "Generalization in reinforcement learning: Successful examples using sparse coarse coding," in Advances in Neural Information Processing Systems 8, pp. 1038-1044, MIT Press, 1996.
8. D. Ernst, P. Geurts, and L. Wehenkel, "Tree-based batch mode reinforcement learning," J. Mach. Learn. Res., vol. 6, pp. 503-556, 2005.
9. A. Antos, R. Munos, and C. Szepesvari, "Fitted Q-iteration in continuous action-space MDPs," in Advances in Neural Information Processing Systems 20, pp. 9-16, Cambridge, MA: MIT Press, 2008.
10. S. Timmer and M. Riedmiller, "Fitted Q-iteration with CMACs," pp. 1-8, 2007.
11. J. Peters and S. Schaal, "Policy learning formotor skills," in Proceedings of 14th International Conference on Neural Information Processing (ICONIP), 2007.
12. P.-T. de Boer, D. Kroese, S. Mannor, and R. Rubinstein, "A tutorial on the cross-entropy method," Annals of Operations Research, vol. 134, pp. 19-67, January 2005. (Pubitemid 40550039)
13. J. Peters and S. Schaal, "Reinforcement learning by reward-weighted regression for operational space control," in Proceedings of the International Conference on Machine Learning (ICML), 2007.
14. C. G. Atkeson, A. W. Moore, and S. Schaal, "Locally weighted learning," Artificial Intelligence Review, vol. 11, no. 1-5, pp. 11-73, 1997. (Pubitemid 127508233)