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

CoRR（2024）

摘要

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 system variables, requiring a rich and flexible
representation of information structure.
In this paper, we argue for the perspective that explicit representation of
information structures is an important component of analyzing and solving
reinforcement learning problems. We propose novel reinforcement learning models
with an explicit representation of information structure, capturing classical
models as special cases. We show that this leads to a richer analysis of
sequential decision-making problems and enables more tailored algorithm design.
In particular, we characterize the "complexity" of the observable dynamics of
any sequential decision-making problem through a graph-theoretic analysis of
the DAG representation of its information structure. The central quantity in
this analysis is the minimal set of variables that d-separates the past
observations from future observations. Furthermore, through constructing a
generalization of predictive state representations, we propose tailored
reinforcement learning algorithms and prove that the sample complexity is in
part determined by the information structure. This recovers known tractability
results and gives a novel perspective on reinforcement learning in general
sequential decision-making problems, providing a systematic way of identifying
new tractable classes of problems.

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