GAWM: Global-aware World Model For Multi-agent Reinforcement Learning

Abstract

In recent years, Model-based Multi-Agent Reinforcement Learning (MARL) has demonstrated significant advantages over model-free methods in terms of sample efficiency by using independent environment dynamics world models for data sample augmentation. However, without considering the limited sample size, these methods still lag behind model-free methods in terms of final convergence performance and stability. This is primarily due to the world model's insufficient and unstable representation of global states in partially observable environments. This limitation hampers the ability to ensure global consistency in the data samples and results in a time-varying and unstable distribution mismatch between the pseudo data samples generated by the world model and the real samples. This issue becomes particularly pronounced in more complex multi-agent environments. To address this challenge, we propose a model-based MARL method called GAWM, which enhances the centralized world model's ability to

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyshi2025gawm

Related papers

• MABL: Bi-level Latent-variable World Model For Sample-efficient Multi-agent Reinforcement Learning (2023)0.00
• Decentralized Transformers With Centralized Aggregation Are Sample-efficient Multi-agent World Models (2024)0.00
• Model-based Multi-agent Reinforcement Learning: Recent Progress And Prospects (2022)0.00
• Efficient Distributed Framework For Collaborative Multi-agent Reinforcement Learning (2022)0.00
• Generative Evolutionary Meta-solver (GEMS): Scalable Surrogate-free Multi-agent Reinforcement Learning (2025)0.00
• Efficient Model-based Multi-agent Reinforcement Learning Via Optimistic Equilibrium Computation (2022)0.00
• On Improving Model-free Algorithms For Decentralized Multi-agent Reinforcement Learning (2021)0.00
• World Models As An Intermediary Between Agents And The Real World (2026)0.00