Transformer World Model for Sample Efficient Multi-Agent Reinforcement Learning
This addresses sample efficiency for multi-agent systems in complex environments, representing a strong incremental advance over prior methods.
The paper tackles sample efficiency in multi-agent reinforcement learning by introducing MATWM, a transformer-based world model that achieves state-of-the-art performance on benchmarks like StarCraft, with near-optimal results in as few as 50K interactions.
We present the Multi-Agent Transformer World Model (MATWM), a novel transformer-based world model designed for multi-agent reinforcement learning in both vector- and image-based environments. MATWM combines a decentralized imagination framework with a semi-centralized critic and a teammate prediction module, enabling agents to model and anticipate the behavior of others under partial observability. To address non-stationarity, we incorporate a prioritized replay mechanism that trains the world model on recent experiences, allowing it to adapt to agents' evolving policies. We evaluated MATWM on a broad suite of benchmarks, including the StarCraft Multi-Agent Challenge, PettingZoo, and MeltingPot. MATWM achieves state-of-the-art performance, outperforming both model-free and prior world model approaches, while demonstrating strong sample efficiency, achieving near-optimal performance in as few as 50K environment interactions. Ablation studies confirm the impact of each component, with substantial gains in coordination-heavy tasks.