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 environment dynamics and
produce 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. We present experimental
results in Easy, Hard, and Super-Hard StarCraft II micro-management challenges
to demonstrate that our method achieves high sample efficiency and exhibits
superior performance in defeating the enemy armies compared to other baselines.

通过提出一种名为价值分解框架与解耦世界模型的新型基于模型的多智能体强化学习方法，本研究解决了多智能体在相同环境下实现共同目标的挑战，降低了样本复杂度，并通过实验结果表明，在 StarCraft II 微管理挑战中，该方法在样本效率和击败敌军方面表现出卓越的性能。