Scalable Model-based Policy Optimization for Decentralized Networked Systems. (arXiv:2207.06559v2 [cs.LG] UPDATED)

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 …

arxiv decentralized optimization policy scalable systems