This document discusses deep reinforcement learning and how it was applied in AlphaGo to master the game of Go. It provides an overview of deep learning, reinforcement learning, and how AlphaGo combined the two approaches. AlphaGo used deep neural networks to mimic human expert moves and play games against itself to estimate win probabilities. It had a policy network to choose moves and a value network to estimate game outcomes. Through deep reinforcement learning, AlphaGo was able to achieve superhuman performance at the game of Go.