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

• URL: http://arxiv.org/abs/2309.04615v1
• Date: Fri, 8 Sep 2023 22:12:43 GMT
• Title: Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning
• Authors: Zhizun Wang and David Meger
• Abstract summary: We propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentangled World Model. 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.
• Score: 18.651307543537655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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