Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method
Nevin Lianwen Zhang, Stephen S. Lee · arXiv (Cornell University) · 2013
There is much interest in using partially observable Markov decision processes (POMDPs) as a formal model for planning in stochastic domains. This paper is concerned with finding optimal policies for POMDPs. We propose several improvements to incremental pruning, presently the most efficient exact algorithm for solving POMDPs.