Solving Partially Observable Markov Decision Processes by Neural Networks.
S. A. Velázquez Lerma, René Alquézar · 1999
Partially Observable Markov Decision Processes #POMDPs# cope with sequential decision processes where an agent tries to maximize or minimize some reward without complete knowledge of the process. These models are of interest for quality control, machine maintenance, reinforcement learning, etc. More generally Monahan #9# has shown that many tasks in partially observable environments can be viewed as POMDPs.