Expert Level control of Ramp Metering based on Multi-task Deep
  Reinforcement Learning
  This article shows how the recent breakthroughs in Reinforcement Learning
(RL) that have enabled robots to learn to play arcade video games, walk or
assemble colored bricks, can be used to perform other tasks that are currently
at the core of engineering cyberphysical systems. We present the first use of
RL for the control of systems modeled by discretized non-linear Partial
Differential Equations (PDEs) and devise a novel algorithm to use
non-parametric control techniques for large multi-agent systems. We show how
neural network based RL enables the control of discretized PDEs whose
parameters are unknown, random, and time-varying. We introduce an algorithm of
Mutual Weight Regularization (MWR) which alleviates the curse of dimensionality
of multi-agent control schemes by sharing experience between agents while
giving each agent the opportunity to specialize its action policy so as to
tailor it to the local parameters of the part of the system it is located in.
