Solving infinite-horizon Dec-POMDPs using Finite State Controllers within JESP

TL;DR

This paper extends the JESP algorithm to infinite-horizon Dec-POMDPs by utilizing finite state controllers, enabling the computation of Nash equilibria in more complex collaborative planning scenarios.

Contribution

It introduces extit{infJESP}, a novel variant of JESP for infinite-horizon Dec-POMDPs using FSCs, along with heuristic initializations and experimental validation.

Findings

Effective in benchmark problems

Outperforms finite-horizon approaches

Provides scalable solutions

Abstract

This paper looks at solving collaborative planning problems formalized as Decentralized POMDPs (Dec-POMDPs) by searching for Nash equilibria, i.e., situations where each agent's policy is a best response to the other agents' (fixed) policies. While the Joint Equilibrium-based Search for Policies (JESP) algorithm does this in the finite-horizon setting relying on policy trees, we propose here to adapt it to infinite-horizon Dec-POMDPs by using finite state controller (FSC) policy representations. In this article, we (1) explain how to turn a Dec-POMDP with $N-1$ fixed FSCs into an infinite-horizon POMDP whose solution is an $N^\text{th}$ agent best response; (2) propose a JESP variant, called \infJESP, using this to solve infinite-horizon Dec-POMDPs; (3) introduce heuristic initializations for JESP aiming at leading to good solutions; and (4) conduct experiments on state-of-the-art benchmark problems to evaluate our approach.