Transformer Based Multi-Agent Framework
Siyi Hu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang · 2021
We present a Transformer-like agent to learn the policy of multi-agent cooperation tasks, which is a breakthrough for traditional RNN-based multi-agent models that need to be retrained for each task. Our model can handle various input and output with strong transferability and can parallel tackle different tasks. Besides, We are the first to successfully utilize transformer into a recurrent architecture, providing insight on stabilizing transformers in recurrent RL tasks.