Deep Reinforcement Learning (DRL) is praised as a potential answer to a multitude of application based problems previously considered too complex for a machine. The resolution of these issues could see wide-scale advances across the industry, including, but not limited to healthcare, robotics and finance.
So where does the ‘Deep’ part of Deep Reinforcement Learning come from? We have heard the term Reinforcement Learning for some time, but now there is a buzz around DRL and rightly so. Simply, a Reinforcement Learning agent becomes a Deep Reinforcement learning agent when layers of artificial neural networks and leveraged somewhere within its algorithm.
Topics: Deep Learning Summit