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
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**"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.
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+Finally, it drops the complexity of the backup operators from double exponential to polynomial at the expense of longer planning horizons.
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+Besides, it makes it easy to use single-agent methods, \eg SARSA algorithm enhanced with these findings, while still preserving convergence guarantees.
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+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.
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+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).
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