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An approximate algorithm, with bounds, for composite-state partially observed Markov decision processes

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1 Author(s)
W. S. Lovejoy ; Graduate Sch. of Bus., Stanford Univ., CA, USA

The author presents an approximate algorithm with bounds, for solving composite-state POMDPs (partially observed Markov decision processes). The approximation is based on a discretization of the unit simplex that has proven effective with conventional POMDPs. The model considered is a composite-state space variation of the discrete-time, finite partially observed Markov decision process with stationary cost data analyzed by R.D. Smallwood and E.J. Sondik (1973). The computational savings achievable with the composite-state construction are indicated

Published in:

Decision and Control, 1990., Proceedings of the 29th IEEE Conference on

Date of Conference:

5-7 Dec 1990