  We consider the problem of designing policies for partially observable Markov
decision processes (POMDPs) with dynamic coherent risk objectives. Synthesizing
risk-averse optimal policies for POMDPs requires infinite memory and thus
undecidable. To overcome this difficulty, we propose a method based on bounded
policy iteration for designing stochastic but finite state (memory)
controllers, which takes advantage of standard convex optimization methods.
Given a memory budget and optimality criterion, the proposed method modifies
the stochastic finite state controller leading to sub-optimal solutions with
lower coherent risk.
