Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems. (arXiv:2202.01534v1 [cs.LG])

Learning effective policies for real-world problems is still an open
challenge for the field of reinforcement learning (RL). The main limitation
being the amount of data needed and the pace at which that data can be
obtained. In this paper, we study how to build lightweight simulators of
complicated systems that can run sufficiently fast for deep RL to be
applicable. We focus on domains where agents interact with a reduced portion of
a larger environment while still being affected …

arxiv deep rl rl systems