Nonparametric model-based reinforcement learning

Advances in Neural Information Processing Systems

Volumn , Issue , 1998, Pages 1008-1014

  Atkeson, Christopher G ^a,b  

^a Georgia Institute of Technology   (United States)

^b ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

MODELS; PLANNING; TRAJECTORIES;

MINIMIZING COSTS; MODEL-BASED REINFORCEMENT LEARNING; NON-PARAMETRIC; OPTIMIZERS; PARAMETRIC MODELS; PLANNING ALGORITHMS;

REINFORCEMENT LEARNING;

EID: 49049119585     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Atkeson, C. G. (1994). Using local trajectory optimizers to speed up global optimization in dynamic programming. In Cowan, J. D., Tesauro, G., and Alspector, J., editors. Advances in Neural Information Processing Systems 6, pages 663-670. Morgan Kaufmann, San Mateo, CA.
2. Atkeson, C. G., Moore, A. W., and Schaal, S. (1997a). Locally weighted learning. Artificial Intelligence Review, 11:11-73.
3. Atkeson, C. G., Moore, A. W., and Schaal, S. (1997b). Locally weighted learning for control. Artificial Intelligence Review, 11:75-113.
4. Atkeson, C. G. and Schaal, S. (1997). Robot learning from demonstration. In Proceedings of the 1997 International Conference on Machine Learning.
5. Barto, A. G., Bradtke, S. J., and Singh, S. P. (1995). Learning to act using real-time dynamic programming. Artificial Intelligence, 72( 1):81-138.
6. Cohen, M. F. (1992). Interactive spacetime control for animation. Computer Graphics, 26(2):293-302.
7. Dyer, P. and McReynolds, S. (1970). The Computational Theory of Optimal Control. Academic, NY.
8. Jacobson, D. and Mayne, D. (1970). Differential Dynamic Programming. Elsevier, NY.
9. Kaelbling, L. P., Littman, M. L., and Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4:237-285.
10. Liu, Z., Gortler, S. J., and Cohen, M. F. (1994). Hierarchical spacetime control. Computer Graphics (S1GGRAPH '94 Proceedings), pages 35-42.
11. Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (1988). Numerical Recipes in C. Cambridge University Press, New York, NY.
12. Schaal, S. (1997). Learning from demonstration. In Mozer, M. C., Jordan, M., and Petsche, T., editors, Advances in Neural Information Processing Systems 9, pages 1040-1046. MIT Press, Cambridge, MA.
13. Spong, M. W. (1995). The swing up control problem for the acrobot. IEEE Control Systems Magazine, 15(1):49-55.
14. Sutton, R. S. (1990). Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Seventh International Machine Learning Workshop, pages 216-224. Morgan Kaufmann, San Mateo, CA. http://envy.cs.umass.edu/People/sutton/publications.html.
15. Sutton R. S. (1991a). Dyna, an integrated architecture for learning, planning and reacting. http://envy.cs.umass.edu/People/sutton/publications.html, Working Notes of the 1991 AAAI Spring Symposium on Integrated Intelligent Architectures pp. 151-155 and SIGART Bulletin 2, pp. 160-163.
16. Sutton, R. S. (1991b). Planning by incremental dynamic programming. In Eighth International Machine Learning Workshop, pages 353-357. Morgan Kaufmann, San Mateo, CA. http://envy.cs.umass.edu/People/sutton/publications.html.
17. Sutton, R. S., Barto, A. G., and Williams, R. J. (1992). Reinforcement learning is direct adaptive optimal control. IEEE Control Systems Magazine, 12:19-22.