If an agent can sense, think, and act in an unknown environment, how MUST it act? Reinforcement Learning (RL) is a general paradigm under which an agent, by trial and error, can discover actions that result in long-term gain. Integrating ideas from various disciplines, RL has matured as a field of study in the last few decades. Its successes range from training programs to decrease the waiting time for elevators in a building; to maximising profits from stock-trading; and to numerous tasks in robotic control and decision-making. In spite of these successes, RL is yet to be counted upon as a practically-viable technology. To a large extent, the gap between the promise of RL and its practical effectiveness has arisen from the lack of suitable representations (and representation-discovery mechanisms) in domains of interest. Interestingly, it is precisely this gap that the emerging paradigm of deep learning is beginning to fill. Deep learning is a data-driven technique that is capable of learning complex non-linear input-output relationships, which are represented using neural networks with a large number of layers (hence ``deep''). The combination of RL with deep learning has registered remarkable successes in recent months. Notably, it has resulted in AI-based agents that exceed human-level performance on a suite of ATARI console games, and also on the more challenging game of Go.