Deep Reinforcement Learning combines reinforcement learning algorithms with high-capacity neural net representations in order to obtain a near optimal control or policy without having a complete model of the world. Through this, the field of reinforcement learning provides a mathematically principled framework to optimize an unknown system without having knowledge of the equations that govern it.