An approximate dynamic programming approach to decentralized control of stochastic systems

Lecture Notes in Control and Information Sciences

Volumn 329, Issue , 2006, Pages 243-256

  Cogill, Randy ^a   Rotkowitz, Michael ^b   Van Roy, Benjamin ^c   Lall, Sanjay ^b  

^a Stanford University   (United States)

^b 496 Lomita Mall   (United States)

^c STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; DYNAMIC PROGRAMMING; OPTIMIZATION; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC SYSTEMS; UNCERTAIN SYSTEMS;

ACTION SPACES; APPROXIMATE DYNAMIC PROGRAMMING; APPROXIMATION ERRORS; CONTROL OF STOCHASTIC SYSTEMS; DECENTRALIZED POLICIES; FINITE STATE; PERFORMANCE LOSS; Q-FUNCTIONS;

DECENTRALIZED CONTROL;

EID: 33748784614     PISSN: 01708643     EISSN: None     Source Type: Book Series    
DOI: 10.1007/11664550_13     Document Type: Article

References (16)

1. J. Tsitsiklis and M. Athans, "On the complexity of decentralized decision making and detection, problems," IEEE Trans. Automatic Control, vol. 30, no. 5, pp. 440-446, 1985.
2. N. Sandell, P. Varayia, M. Athens, and M. Safonov, "Survey of decentralized control methods for large-scale systems," IEEE Trans. Automatic Control, vol. 26, pp. 108-128, 1978.
3. C. Guestrin, D. Koller, and R. Parr, "Multiagent planning with factored MDPs," Advances in Neural Information Processing Systems, vol. 14, pp. 1523-1.530, 2001.
4. J. Bernussou, P. Peres, and J. Geromel, "Robust decentralized regulation: a linear programming approach," Proc. IFAC Symposium on Large Scale Systems, vol. 1, pp. 133-136, 1989.
5. D. Rertsekas and J. Tsilsiklis, Neuro-dynamic Programming. Athena Scientific, 1996.
6. D. de Parias and B. V. Roy, "The linear programming approach to approximate dynamic programming," Operations Research, vol. 51, no. 6, pp. 850-865, 2003.
7. A. Manne, "Linear programming and sequential decisions," Management Science, vol. 6, no. 3, pp. 259-267, 1960.
8. D. de Farias and B. V. Roy, "On constraint sampling in the linear programming approach to approximate dynamic programming," to appear in Mathematics of Operations Research, submitted 2001.
9. M. Aicardi, F. Davoid, and R. Minciardi, "Decentralized optimal control of Markov chains with a common past information set," IEEE Trans. Automatic Control, vol. 32, no. 11, pp. 1028-1031, 1987.
10. R. Cogill, M. Rotkowitz, B. V. Roy, and S. Lall, "An approximate dynamic programming approach to decentralized control of stochastic systems," Proceedings of the 2004 Allerton Conference on Communication, Control, and Computing, pp. 1040-1049, 2004.
11. Y. Ho, "Team decision theory and information structures," Proceedings of the IEEE, vol. 68, no. 6, pp. 644-654, 1980.
12. K. Hsu and S. Marcus, "Decentralized control of finite state Markov processes," IEEE Trans. Automatic Control, vol. 27, no. 2, pp. 426-431, 1982.
13. C. Papadimitriou and J. Tsitsiklis, "Intractable problems in control theory," SIAM Journal on Control and Optimization, vol. 24, no. 4, pp. 639-654, 1986.
14. _, "The complexity of Markov decision processes," Mathematics of operations research, vol. 12, no. 3, pp. 441-450, 1987.
15. M. Puterman, Markov decision processes. John Wiley and Sons, New York, 1994.
16. R. Srikant and T. Başar, "Optimal solutions in weakly coupled multiple decision maker Markov chains with nonclassical information," Proceedings of the IEEE Conference on Decision and Control, pp. 168-173, 1989.