Over the past few years, the AI community has witnessed huge breakthroughs since the development of deep reinforcement learning. For example, using reinforcement learning scientists developed software that learned how to achieve super-human skill in playing different Atari games directly from raw-pixels without being given any game-specific instructions, and the AlphaGo AI system from DeepMind used reinforcement learning to master the Go game and beat the best human champion.
Reinforcement learning is a branch of artificial intelligence that deals with teaching machines how to choose best actions to perform while facing uncertainty. It has a lot of applications in robotics, recommendation systems, and autonomous systems such as self-driving cars.
In this tutorial, we are going to provide an explanation about reinforcement learning from the ground-up covering the fundamental theory and algorithms. We will also describe how RL meets with the powerful deep-neural networks to form "deep reinforcement learning". In addition, we will demonstrate code examples that show how to apply these algorithms to solve AI problems.
