Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
Keywords: multi-agent reinforcement learning, world models, learning in imagination
TL;DR: We introduce a CTDE-inspired Transformer-based multi-agent world model for sample-efficient multi-agent policy learning in its imaginations.
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. Main results on Starcraft Multi-Agent Challenge (SMAC) and additional results on MAMujoco show that it outperforms strong model-free approaches and existing model-based methods in both sample efficiency and overall performance.
Submission Number: 34
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