  This paper provides a relaxation of the sufficient conditions, and also an
extension of the structural results for Partially Observed Markov Decision
Processes (POMDPs) given in Lovejoy (1987). Sufficient conditions are provided
so that the optimal policy can be upper and lower bounded by judiciously chosen
myopic policies. These myopic policy bounds are constructed to maximize the
volume of belief states where they coincide with the optimal policy. Numerical
examples illustrate these myopic bounds for both continuous and discrete
observation sets.
