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Consolidated actor-critic model for partially-observable Markov decision processes

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4 Author(s)
Elhanany, I. ; Dept. of Electr. Eng. & Comput. Sci., Univ. of Tennessee, Knoxville, TN ; Niedzwiedz, C. ; Liu, Z. ; Livingston, S.

A method for consolidating the traditionally separate actor and critic neural networks in temporal difference learning for addressing partially-observable Markov decision processes (POMDPs) is presented. Simulation results for solving a five-state POMDP problem support the claim that the consolidated model achieves higher performance while reducing computational and storage requirements to approximately half those of the traditional approach.

Published in:

Electronics Letters  (Volume:44 ,  Issue: 22 )