Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models

• URL: http://arxiv.org/abs/2406.15836v1
• Date: Sat, 22 Jun 2024 12:40:03 GMT
• Title: Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
• Authors: Yang Zhang, Chenjia Bai, Bin Zhao, Junchi Yan, Xiu Li, Xuelong Li,
• Abstract summary: We propose a novel world model for Multi-Agent RL (MARL) that learns decentralized local dynamics for scalability. We also introduce a Perceiver Transformer as an effective solution to enable centralized representation aggregation. Results on Starcraft Multi-Agent Challenge (SMAC) show that it outperforms strong model-free approaches and existing model-based methods in both sample efficiency and overall performance.
• Score: 106.94827590977337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning a world model for model-free Reinforcement Learning (RL) agents can significantly improve the sample efficiency by learning policies in imagination. However, building a world model for Multi-Agent RL (MARL) can be particularly challenging due to the scalability issue in a centralized architecture arising from a large number of agents, and also the non-stationarity issue in a decentralized architecture stemming from the inter-dependency among agents. To address both challenges, we propose a novel world model for MARL that learns decentralized local dynamics for scalability, combined with a centralized representation aggregation from all agents. We cast the dynamics learning as an auto-regressive sequence modeling problem over discrete tokens by leveraging the expressive Transformer architecture, in order to model complex local dynamics across different agents and provide accurate and consistent long-term imaginations. As the first pioneering Transformer-based world model for multi-agent systems, we introduce a Perceiver Transformer as an effective solution to enable centralized representation aggregation within this context. Results on Starcraft Multi-Agent Challenge (SMAC) show that it outperforms strong model-free approaches and existing model-based methods in both sample efficiency and overall performance.

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