Prioritized Asynchronous Belief Propagation

Message scheduling is shown to be very eective in belief propagation (BP) algorithms. However, most existing scheduling algorithms use xed heuristics regardless of the structure of the graphs or properties of the distribution. On the other hand, designing dierent scheduling heuristics for all graph structures are not feasible. In this paper, we propose a reinforcement learning based message scheduling framework (RLBP) to learn the heuristics automatically which generalizes to any graph structures and distributions. In the experiments, we show that the learned problem-specic heuristics largely outperform other baselines in speed.