A few weeks removed from the recent CNC use case we demonstrated with Siemens (reducing machine calibration time by 30x) I thought it would be valuable to take a step back, add some historical context to the importance of this milestone, and start to explore what this could mean for the future of deep reinforcement learning (DRL). In contrast to some of the other most frequently cited achievements in DRL, these results show the tremendous business value that can be realized when this technology is applied to real world systems. To explore this achievement in greater detail I recently wrote a post over at insideBIGDATA titled “Deep Reinforcement Learning: From Board Games to the Boardroom”. In this article I lay out the trajectory DRL has been following so far, the obstacles it has encountered along the way, and explain where it may be going from here. Head over, take a read, and let me know what you think. In the meantime, if you are interested in getting your hands on our platform to see how DRL can help optimize the performance of your application you can get started here.