Dynamic programming for ergodic control with partial observations

Stochastic Processes and their Applications

Volumn 103, Issue 2, 2003, Pages 293-310

  Borkar, V S ^a  

^a TATA INSTITUTE OF FUNDAMENTAL RESEARCH   (India)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0037290932     PISSN: 03044149     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-4149(02)00190-4     Document Type: Article

References (23)

1. Aronson, D.G., 1967. Bounds for the fundamental solution of a parabolic equation. Bull. Amer. Math. Soc. 73, 890-896.
2. Athreya, K.B., Ney, P., 1978. A new approach to the limit theory of recurrent Markov chains. Trans. Amer. Math. Soc. 245, 493-501.
3. Bhatt, A.G., Borkar, V.S., 1996. Occupation measures for controlled Markov processes: characterization and optimality. Ann. Probab. 24, 1531-1562.
4. Billingsley, P., 1968. Convergence of Probability Measures. Wiley, New York.
5. Borkar, V.S., 1986. A remark on attainable distributions of controlled diffusions. Stochastics 18, 17-23.
6. Borkar, V.S., 1989. Optimal Control of Diffusion Processes. Pitman Research Notes in Maths, No. 203. Longman Scientific and Technical, Harlow, England.
7. Borkar, V.S., 1993. On white noise representations in stochastic realization theory. SIAM J. Control Optim. 31, 1093-1102.
8. Borkar, V.S., 1995. Probability Theory: An Advanced Course. Springer, New York.
9. Borkar, V.S., 1999. The value function in ergodic control of diffusion processes with partial observations. Stochastics Stochastics Rep. 67, 255-266.
10. Borkar, V.S., 2000a. Average cost dynamic programming equations for controlled Markov chains with partial observations. SIAM J. Control Optim. 39, 673-681.
11. Borkar, V.S. 2000b. The value function in ergodic control of diffusion processes with partial observations II. Appl. Math. 27, 455-464. (Correction note in Appl. Math., 28, 2001, 245-246).
12. Davis, M.H.A., Varaiya, P.P., 1973. Dynamic programming conditions for partially observable stochastic systems. SIAM J. Control Optim. 11, 226-261.
13. Fleming, W.H., Pardoux, E., 1982. Optimal control of partially observed diffusions. SIAM J. Control Optim. 20, 261-285.
14. Gy'ongi, I., 1986. Mimicking one dimensional marginal distributions of processes having an Ito differential. Prob. Theory Related Fields 71, 501-516.
15. Hernández-Lerma, O., Lasserre, J.B., 1996. Discrete-Time Markov Control Processes: Basic Optimality Criteria. Springer, New York.
16. Hernández-Lerma, O., Lasserre, J.B., 1999. Further Topics on Discrete-Time Markov Control Processes. Springer, New York.
17. Ikeda, N., Watanabe, S., 1981. Stochastic Differential Equations and Diffusion Processes. North-Holland, Kodansha, Amsterdam, Tokyo.
18. Ladyzenskaja, O.A., Ural'seva, N.N., Solonnikov, V.A., 1968. Linear and Quasilinear Equations of Parabolic Type. Transactions of Mathematical Monographs 23. A.M.S., Providence, RI.
19. Meyn, S.P., Tweedie, R.L., 1993. Markov Chains and Stochastic Stability. Springer, London.
20. Nummelin, E., 1978. A splitting technique for Harris recurrent chains. Z. Wahrscheinlichkeitstheorie Verw. Geb. 43, 309-318.
21. Rishel, R., 1970. Necessary and sufficient dynamic programming conditions for continuous time stochastic control. SIAM J. Control Optim. 8, 559-571.
22. Striebel, C., 1984. Martingale methods for the optimal control of continuous time stochastic systems. Stochastic Process. Appl. 18, 329-347.
23. Wagner, D.H., 1977. Survey of measurable selection theorems. SIAM J. Control Optim. 15, 859-903.