Learning multi-modal control programs

Lecture Notes in Computer Science

Volumn 3414, Issue , 2005, Pages 466-479

  Mehta, Tejas R ^a   Egerstedt, Magnus ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; CONTROL SYSTEMS; FINITE AUTOMATA; SET THEORY; SIGNAL PROCESSING;

DESIGN TOOLS; FINITE STATE MACHINES; MULTI-MODE CONTROL PROGRAMS; Q-LEARNING; STATE-SPACE EXPLORATIONS;

LEARNING SYSTEMS;

EID: 24344437333     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-31954-2_30     Document Type: Conference Paper

References (17)

1. A. Bhatia, and E. Frazzoli. Incremental Search Methods for Reachability Analysis of Continuous and Hybrid Systems. Hybrid Systems: Computation and Control. Springer- Verlag, 2004.
2. A. Bicchi, A. Marigo, and B. Piccoli. On the reachability of quantized control systems. IEEE Transactions on Automatic Control, 4(47):546-563, April 2002.
3. . A. Bicchi, A. Marigo, and B. Piccoli. Encoding steering control with symbols. IEEE International Conference on Decision and Control, pages 3343-3348, 2003.
4. S.J. Bradtke, B.E. Ydstie, and A.G. Barto. Adaptive linear quadratic control using policy iteration. American Control Conference, pages 3475-3479, 1994.
5. L. Crawford, and S.S. Sastry. Learning Controllers for Complex Behavioral Systems. Neural Information Processing Systems Tenth Annual Conference (NIPS 96), 1996.
6. M. Egerstedt. On the Specification Complexity of Linguistic Control Procedures. International Journal of Hybrid Systems, Vol. 2, No. 1-2, pp. 129-140, March & June, 2002.
7. M. Egerstedt, and D. Hristu-Varsakelis. Observability and Policy Optimization for Mobile Robots. IEEE Conference on Decision and Control, Las Vegas, NV, Dec. 2002.
8. M. Egerstedt, and R.W. Brockett. Feedback Can Reduce the Specification Complexity of Motor Programs. IEEE Transactions on Automatic Control, Vol. 48, No. 2, pp. 213-223, Feb. 2003
9. T. Jaakkola, M.I. Jordan, and S.P. Singh. On the Convergence of stochastic iterative dynamic programming algorithms. Neural Computation 6(6), 1994.
10. L.P. Kaebling, M.L. Littman, and A.R. Cassandra. Learning Policies for Partially Observable Environments: Scaling Up. Proceedings of the Twelfth International Conference on Machine Learning, 1995.
11. L.P. Kaebling, M.L. Littman, and A.W. Moore. Reinforcement learning: A survey. Journal Of Artificial Intelligence Research, 1996.
12. K. Morgansen, and R.W. Brockett. Optimal Regulation and Reinforcement Learning for the Nonholonomic Integrator. Proceedings of the American Control Conference, pp. 462-6, June 2000
13. R.S. Sutton. Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. Neural Information Processing Systems 8, 1996.
14. R.S. Sutton, and A.G. Barto. Reinforcement Learning, An Introduction. MIT Press, Cambridge, MA, 1998.
15. P. Tabuada and G. Pappas. Model Checking LTL over Controllable Linear Systems is Decidable. Hybrid Systems: Computation and Control, Springer-Verlag, Prague, Czech Republic, 2003.
16. J.N. Tsitsiklis. Asynchronous stochastic approximation and Q-learning. Machine Learning, 16(3), 1994.
17. C.J.C.H. Watkins, and P. Dayan. Q-learning. Machine Learning 8(3/4):257-277, May 1992.