Transformer World Model for Sample Efficient Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2506.18537v1
• Date: Mon, 23 Jun 2025 11:47:17 GMT
• Title: Transformer World Model for Sample Efficient Multi-Agent Reinforcement Learning
• Authors: Azad Deihim, Eduardo Alonso, Dimitra Apostolopoulou,
• Abstract summary: We present the Multi-Agent Transformer World Model (MATWM), a novel transformer-based world model for reinforcement learning.<n>MATWM combines a decentralized imagination framework with a semi-centralized critic and a teammate prediction module.<n>We evaluate MATWM on a broad suite of benchmarks, including the StarCraft Multi-Agent Challenge, PettingZoo, and MeltingPot.
• Score: 2.3964255330849356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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