From 72ccde4f0d32b08880f62921b76ea61e40c338a2 Mon Sep 17 00:00:00 2001
From: Rafael Fernandes Cunha <r.f.cunha@rug.nl>
Date: Wed, 18 Dec 2024 11:56:54 +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.
-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.  
-
-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.
+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. 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. 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
 
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GitLab