Escaping local optima in POMDP planning as inference

Planning as inference recently emerged as a versatile approach to decision-theoretic planning and reinforcement learning for single and multi-agent systems in fully and partially observable domains with discrete and continuous variables. Since planning as inference essentially tackles a non-convex optimization problem when the states are partially observable, there is a need to develop techniques that can robustly escape local optima. We propose two algorithms: the first one adds nodes to the controller according to an increasingly deep forward search, while the second one splits nodes in a greedy fashion to improve reward likelihood.