Nonparametric Representation of Policies and Value Functions: A Trajectory-Based Approach

NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems

Volumn , Issue , 2002, Pages 1611-1618

  Atkeson, Christopher G ^a   Morimoto, Jun ^b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

REINFORCEMENT LEARNING;

CURSE OF DIMENSIONALITY; DISCOUNT FACTORS; FUNCTIONS APPROXIMATIONS; NONPARAMETRIC REPRESENTATION; PERIODIC TASKS; RAPID LEARNING; REINFORCEMENT LEARNINGS; TRAJECTORY FUNCTIONS; TRAJECTORY-BASED; VALUE FUNCTIONS;

TRAJECTORIES;

EID: 85156195508     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Richard S. Sutton. Integrated architectures for learning, planning and reacting based on approximating dynamic programming. In Proceedings 7th International Conference on Machine Learning., 1990.
2. C. Atkeson and J. Santamaria. A comparison of direct and model-based reinforcement learning, 1997.
3. Christopher G. Atkeson. Using local trajectory optimizers to speed up global optimization in dynamic programming. In Jack D. Cowan, Gerald Tesauro, and Joshua Alspector, editors, Advances in Neural Information Processing Systems, volume 6, pages 663-670. Morgan Kaufmann Publishers, Inc., 1994.
4. P. Dyer and S. R. McReynolds. The Computation and Theory of Optimal Control. Academic Press, New York, NY, 1970.
5. D. H. Jacobson and D. Q. Mayne. Differential Dynamic Programming. Elsevier, New York, NY, 1970.
6. Christopher G. Atkeson and Stefan Schaal. Robot learning from demonstration. In Proc. 14th International Conference on Machine Learning, pages 12-20. Morgan Kaufmann, 1997.
7. C. G. Atkeson, A. W. Moore, and S. Schaal. Locally weighted learning. Artificial Intelligence Review, 11:11-73, 1997.
8. W. Schwind and D. Koditschek. Control of forward velocity for a simplified planar hopping robot. In International Conference on Robotics and Automation, volume 1, pages 691-6, 1995.
9. J. Andrew Bagnell and Jeff Schneider. Autonomous helicopter control using reinforcement learning policy search methods. In International Conference on Robotics and Automation, 2001.
10. M. Garcia, A. Chatterjee, and A. Ruina. Efficiency, speed, and scaling of two-dimensional passive-dynamic walking. Dynamics and Stability of Systems, 15(2):75-99, 2000.
11. K. Zhou, J. C. Doyle, and K. Glover. Robust Optimal Control. PRENTICE HALL, New Jersey, 1996.
12. J. Morimoto and K. Doya. Robust Reinforcement Learning. In Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors, Advances in Neural Information Processing Systems 13, pages 1061-1067. MIT Press, Cambridge, MA, 2001.
13. R. Neuneier and O. Mihatsch. Risk Sensitive Reinforcement Learning. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11, pages 1031-1037. MIT Press, Cambridge, MA, USA, 1998.
14. S. P. Coraluppi and S. I. Marcus. Risk-Sensitive and Minmax Control of Discrete-Time Finite-State Markov Decision Processes. Automatica, 35:301-309, 1999.
15. J. Morimoto and C. Atkeson. Minimax differential dynamic programming: An application to robust biped walking. In Advances in Neural Information Processing Systems 15. MIT Press, Cambridge, MA, 2002.