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Scalable Model-based Policy Optimization for Decentralized Networked Systems. (arXiv:2207.06559v2 [cs.LG] UPDATED)
Sept. 5, 2022, 1:12 a.m. | Yali Du, Chengdong Ma, Yuchen Liu, Runji Lin, Hao Dong, Jun Wang, Yaodong Yang
cs.LG updates on arXiv.org arxiv.org
Reinforcement learning algorithms require a large amount of samples; this
often limits their real-world applications on even simple tasks. Such a
challenge is more outstanding in multi-agent tasks, as each step of operation
is more costly requiring communications or shifting or resources. This work
aims to improve data efficiency of multi-agent control by model-based learning.
We consider networked systems where agents are cooperative and communicate only
locally with their neighbors, and propose the decentralized model-based policy
optimization framework (DMPO). In …
More from arxiv.org / cs.LG updates on arXiv.org
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