Can global awareness improve MARL's sample efficiency?
GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning
This paper introduces GAWM, a new approach to model-based multi-agent reinforcement learning (MARL) that improves how AI agents learn and make decisions in environments with multiple agents. It addresses the problem of inconsistent and inaccurate predictions in existing models by focusing on creating a shared, accurate understanding of the overall environment state. This is achieved by combining observations from all agents using a transformer architecture, similar to how LLMs process text. This shared understanding helps in generating more consistent training data and leads to better coordination among the agents. Relevant to LLM-based multi-agent systems, GAWM demonstrates the effectiveness of using transformer-like architectures for information fusion, highlighting its potential for improving global state representation, a critical aspect for effective multi-agent collaboration. Additionally, the use of "trend modeling" for rewards, rather than precise prediction, simplifies reward modeling and enhances training stability, which could be valuable in complex LLM-driven multi-agent scenarios.