Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning

no code implementations • 8 Sep 2023 • Zhizun Wang, David Meger

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.

Disentanglement Management +4