arxiv.orgpaper

A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing

This arXiv preprint proposes a decentralized learning and planning algorithm for team decision problems modeled as partially observable Markov decision processes with unknown, low-rank latent dynamics. Each team member acts only on local private information plus delayed common information, learning an approximate low-rank MDP and applying least-squares value iteration to compute its policy without any centralized coordinator or centralized training. The authors prove the resulting decentralized policies approximate the centralized team-optimal solution and derive finite-sample and sample-complexity guarantees.