MIT (2020): Reinforcement Learning

MIT Introduction to Deep Learning : Lecture 5 Deep Reinforcement Learning Lecturer: Alexander Amini January 2020 For all lectures, slides, and lab materials: Lecture Outline 0:00 - Introduction 2:47 - Classes of learning problems 4:59 - Definitions 9:23 - The Q function 13:18 - Deeper into the Q function 17:17 - Deep Q Networks 21:44 - Atari results and limitations 24:13 - Policy learning algorithms 27:36 - Discrete vs continuous actions 30:11 - Training policy gradients 36:04 - RL in real life 37:40 - VISTA simulator 38:55 - AlphaGo and AlphaZero 42:51 - Summary Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!