Publications [#338706] of Lawrence Carin

Papers Published

1. Liu, M; Amato, C; Liao, X; Carin, L; How, JP, Stick-breaking policy learning in Dec-POMDPs, IJCAI International Joint Conference on Artificial Intelligence, vol. 2015-January (January, 2015), pp. 2011-2018, ISBN 9781577357384
   (last updated on 2024/12/31)

   Abstract:
   Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often converge to maxima that are far from the optimal value. This paper represents the local policy of each agent using variable-sized FSCs that are constructed using a stick-breaking prior, leading to a new framework called decentralized stick-breaking policy representation (Dec-SBPR). This approach learns the controller parameters with a variational Bayesian algorithm without having to assume that the Dec-POMDP model is available. The performance of Dec-SBPR is demonstrated on several benchmark problems, showing that the algorithm scales to large problems while outperforming other state-of-the-art methods.