Abstract: Reinforcement Learning (RL) has garnered renewed attention due to demonstrations of super-human performance in video and board games, which have arisen largely due to capabilities afforded by the marriage of deep neural networks with tradition reinforcement learning (‘deep RL’). However, the use of deep RL within most practical control situations is almost non-existent, due to difficulties with sample efficiency and the associated need for enormous volumes of training data. The situation is even more pronounced within materials science, where basic ML approaches are only now becoming adopted. In this talk, we will explore the landscape of using deep RL within materials synthesis, focusing on the task of growing thin films with pulsed laser deposition with desired morphologies. The problem is presented as a Markov Decision Process with incomplete information, with delayed feedback, and therefore a candidate for deep RL methods. Challenges associated with the appropriate state and reward functions are presented. Results on use of deep Q learning for morphological control with a kinetic Monte-Carlo simulation are discussed. Success will require not only improvements in current policy learning methods but advances in accelerating simulations of film growth on high performance computing environments, and automated synthesis platforms.