Scalable POMDP Decision-Making Using Circulant Controllers

This paper presents a novel policy representation for partially observable Markov decision processes (POMDPs) called circulant controllers and a provably efficient gradient-based algorithm for them. A formal mathematical description is provided that leverages circulant matrices for the controller's stochastic node transitions. This structure is particularly effective for capturing decision-making patterns found in real-world domains with repeated periodic behaviors that adapt their cycles based on observation. This includes domains such as bipedal walking over varied terrain, pick-and-place tasks in warehouses, and home healthcare monitoring and medicine delivery in household environments. A performant gradient-based algorithm is presented with a detailed theoretical analysis, formally proving the algorithm's improved performance, as well as circulant controllers' structural properties. Experiments on these domains demonstrate that the proposed controller algorithm outperforms other state-of-the-art POMDP controller algorithms. The proposed novel controller approach is demonstrated on an actual robot performing a navigation task in a real household environment.