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Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning
Zhiwei Xu · dapeng li · Bin Zhang · Yuan Zhan · Yunpeng Baiia · Guoliang Fan

Thu Dec 08 09:00 AM -- 11:00 AM (PST) @

Recently, model-based agents have achieved better performance than model-free ones using the same computational budget and training time in single-agent environments. However, due to the complexity of multi-agent systems, it is tough to learn the model of the environment. The significant compounding error may hinder the learning process when model-based methods are applied to multi-agent tasks. This paper proposes an implicit model-based multi-agent reinforcement learning method based on value decomposition methods. Under this method, agents can interact with the learned virtual environment and evaluate the current state value according to imagined future states in the latent space, making agents have the foresight. Our approach can be applied to any multi-agent value decomposition method. The experimental results show that our method improves the sample efficiency in different partially observable Markov decision process domains.

Author Information

Zhiwei Xu (Institute of Automation, Chinese Academy of Sciences)
dapeng li (Institute ofAutomation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences)
Bin Zhang (Institute of automation, Chinese academy of science, Chinese Academy of Sciences)
Yuan Zhan (Institute of automation, Chinese academic of science)
Yunpeng Baiia (Institute of automation, Chinese academy of science, Chinese Academy of Sciences)
Guoliang Fan (Institute of automation, Chinese academy of science, Chinese Academy of Sciences)

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