Design for an Optimal Probe

Proceedings, Twentieth International Conference on Machine Learning

Volumn 1, Issue , 2003, Pages 131-138

  Duff, Michael ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS; MARKOV PROCESSES; OPTIMIZATION; PROBABILISTIC LOGICS; PROCESS CONTROL; UNCERTAIN SYSTEMS;

BAYESIAN FORMULATION; CARDINALITY GROWTH; MARKOV DECISION PROCESS;

DECISION THEORY;

EID: 1942421168     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Bellman, R. (1961) Adaptive Control Proceses: A Guided Tour. Princeton Univ. Press, Princeton, NJ.
2. DeGroot, M. H. (1970) Optimal Statistical Decisions, McGraw-Hill, New York.
3. Duff, M. O. (2002) Optimal learning: Computational procedures for Bayes-adaptive Markov decision processes. Ph.D. Thesis. Dept. of Computer Science, Univ. of Massachusetts, Amherst.
4. Konda, V. & Tsitsiklis, J. (2000). Actor-critic algorithms. In S.A. Solla, T. K. Leen, & K. R. Muller (Eds.), Advances in Neural Information Processing Systems - 12. MIT Press, Cambridge, MA.
5. Martin, J. J. (1967). Bayesian Decision Problems and Markov Chains. John Wiley & Sons, New York.
6. Silver, E. A. (1963). Markovian decision processes wih uncertain transition probabilities or rewards (Technical Report No. 1). Research in the Control of Complex Systems, Operations Research Center, MIT.
7. Sutton, R. S., McAllester, D., Singh, S., & Mansour, Y. (2000). Policy gradient methods for reinforcement learning with function approximation. In S.A. Solla, T. K. Leen, & K. R. Muller (Eds.), Advances in Neural Information Processing Systems-12 (pp. 1057-1063). MIT Press, Cambridge, MA.