OPTIMIZATION FOR STOCHASTIC, PARTIALLY OBSERVED SYSTEMS USING A SAMPLING-BASED APPROACH TO LEARN SWITCHED POLICIES BY

We propose a new method for learning policies for large, partially observ-able Markov decision processes (POMDPs) that require long time horizons for planning. Computing optimal policies for POMDPs is an intractable problem and, in practice, dimensionality renders exact solutions essentially unreachable for even small real-world systems of interest. For this reason, we restrict the policies we learn to the class of switched belief-feedback policies in a manner that allows us to introduce domain expert knowledge into the planning process. This approach has worked well for the systems on which we have tested our approach, and we conjecture that it will be useful for many real-world systems of interest. Our approach is based on a method like value iteration to learn a switching law. Because the POMDP problem is intractable, we use a Monte Carlo ap-proximation to evaluate system behavior and optimize a switching law based on sampling. We explicitly analyze the sensitivity of expected cost (per