From 76c1610a01620930a09012b53a8c4cf392990a50 Mon Sep 17 00:00:00 2001 From: Rafael Fernandes Cunha <r.f.cunha@rug.nl> Date: Wed, 18 Dec 2024 11:54:38 +0000 Subject: [PATCH] abstract --- README.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/README.md b/README.md index 093afa7..a33de51 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,24 @@ This repository contains the C++ implementation accompanying the AAAI-25 confere **"Optimally Solving Simultaneous-Move Dec-POMDPs: The Sequential Central Planning Approach"** +The centralized training for decentralized execution paradigm emerged as the state-of-the-art approach to $\epsilon$-optimally solving decentralized partially observable Markov decision processes. However, scalability remains a significant issue. + +This paper presents a novel and more scalable alternative, namely the sequential-move centralized training for decentralized execution. + +This paradigm further pushes the applicability of \citeauthor{bellman}'s principle of optimality, raising three new properties. + +First, it allows a central planner to reason upon sufficient sequential-move statistics instead of prior simultaneous-move ones. + +Next, it proves that $\epsilon$-optimal value functions are piecewise linear and convex in such sufficient sequential-move statistics. + +Finally, it drops the complexity of the backup operators from double exponential to polynomial at the expense of longer planning horizons. + +Besides, it makes it easy to use single-agent methods, \eg SARSA algorithm enhanced with these findings, while still preserving convergence guarantees. + +Experiments on two- as well as many-agent domains from the literature against $\epsilon$-optimal simultaneous-move solvers confirm the superiority of our novel approach. + +This paradigm opens the door for efficient planning and reinforcement learning methods for multi-agent systems. + ## Overview This project provides the source code and example experiments used to implement and evaluate the proposed Sequential Central Planning (SCP) approach for optimally solving Simultaneous-Move Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs). -- GitLab