Model-Based Reinforcement Learning Control of Reaction-Diffusion
  Problems
Christina Schenk Aditya Vasudevan Maciej Haranczyk Ignacio Romero
Mathematical and computational tools have proven to be reliable in
decision-making processes. In recent times, in particular, machine
learning-based methods are becoming increasingly popular as advanced support
tools. When dealing with control problems, reinforcement learning has been
applied to decision-making in several applications, most notably in games. The
success of these methods in finding solutions to complex problems motivates the
exploration of new areas where they can be employed to overcome current
difficulties. In this paper, we explore the use of automatic control strategies
to initial boundary value problems in thermal and disease transport.
Specifically, in this work, we adapt an existing reinforcement learning
algorithm using a stochastic policy gradient method and we introduce two novel
reward functions to drive the flow of the transported field. The new
model-based framework exploits the interactions between a reaction-diffusion
model and the modified agent. The results show that certain controls can be
implemented successfully in these applications, although model simplifications
had to be assumed.