Deep reinforcement learning
Deep reinforcement learning uses deep learning and reinforcement learning principles to create efficient algorithms applied on areas like robotics, video games, NLP, computer vision, education, transportation, finance and healthcare. Implementing deep learning architectures with reinforcement learning algorithms is capable of scaling to previously unsolvable problems. That is because DRL is able to learn from raw sensors or image signals as input. A remarkable milestone in DQN is that agent uses end-to-end reinforcement learning with convolutional neural networks for playing ATARI games.
