  The transformation towards renewable energy and feedstock supply in the
chemical industry requires new conceptual process design approaches. Recently,
breakthroughs in artificial intelligence offer opportunities to accelerate this
transition. Specifically, deep reinforcement learning, a subclass of machine
learning, has shown the potential to solve complex decision-making problems and
aid sustainable process design. We survey state-of-the-art research in
reinforcement learning for process design through three major elements: (i)
information representation, (ii) agent architecture, and (iii) environment and
reward. Moreover, we discuss perspectives on underlying challenges and
promising future works to unfold the full potential of reinforcement learning
for process design in chemical engineering.
