On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games

arXiv:2403.00993v1 Announce Type: new
Abstract: In a sequential decision-making problem, the information structure is the description of how events in the system occurring at different points in time affect each other. Classical models of reinforcement learning (e.g., MDPs, POMDPs, Dec-POMDPs, and POMGs) assume a very simple and highly regular information structure, while more general models like predictive state representations do not explicitly model the information structure. By contrast, real-world sequential decision-making problems typically involve a complex and time-varying interdependence of …

abstract arxiv cs.ai cs.lg decision events games information making observable reinforcement reinforcement learning role stat.ml teams the information type