VDFD: Multi-Agent Value Decomposition Framework with Disentangled World Model

• URL: http://arxiv.org/abs/2309.04615v2
• Date: Thu, 25 Sep 2025 23:51:30 GMT
• Title: VDFD: Multi-Agent Value Decomposition Framework with Disentangled World Model
• Authors: Zhizun Wang, David Meger,
• Abstract summary: We propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentangled World Model.<n>Our method achieves high sample efficiency and exhibits superior performance compared to other baselines across a wide range of multi-agent learning tasks.
• Score: 10.36125908359289
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentangled World Model to address the challenge of achieving a common goal of multiple agents interacting in the same environment with reduced sample complexity. Due to scalability and non-stationarity problems posed by multi-agent systems, model-free methods rely on a considerable number of samples for training. In contrast, we use a modularized world model, composed of action-conditioned, action-free, and static branches, to unravel the complicated environment dynamics. Our model produces imagined outcomes based on past experience, without sampling directly from the real environment. We employ variational auto-encoders and variational graph auto-encoders to learn the latent representations for the world model, which is merged with a value-based framework to predict the joint action-value function and optimize the overall training objective. Experimental results on StarCraft II micro-management, Multi-Agent MuJoCo, and Level-Based Foraging challenges demonstrate that our method achieves high sample efficiency and exhibits superior performance compared to other baselines across a wide range of multi-agent learning tasks.

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