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Recent advances in multi-agent reinforcement learning have been largelylimited in training one model from scratch for every new task. The limitationis due to the restricted model architecture related to fixed input and outputdimensions. This hinders the experience accumulation and transfer of thelearned agent over tasks with diverse levels of difficulty (e.g. 3 vs 3 or 5 vs6 multi-agent games). In this paper, we make the first attempt to explore auniversal multi-agent reinforcement learning pipeline, designing one singlearchitecture to fit tasks with the requirement of different observation andaction configurations. Unlike previous RNN-based models, we utilize atransformer-based model to generate a flexible policy by decoupling the policydistribution from the intertwined input observation with an importance weightmeasured by the merits of the self-attention mechanism. Compared to a standardtransformer block, the proposed model, named as Universal Policy DecouplingTransformer (UPDeT), further relaxes the action restriction and makes themulti-agent task's decision process more explainable. UPDeT is general enoughto be plugged into any multi-agent reinforcement learning pipeline and equipthem with strong generalization abilities that enables the handling of multipletasks at a time. Extensive experiments on large-scale SMAC multi-agentcompetitive games demonstrate that the proposed UPDeT-based multi-agentreinforcement learning achieves significant results relative tostate-of-the-art approaches, demonstrating advantageous transfer capability interms of both performance and training speed (10 times faster).

UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers