MIT : Reinforcement Learning

MIT Introduction to Deep Learning : Lecture 5 Deep Reinforcement Learning Lecturer: Alexander Amini January 2021 For all lectures, slides, and lab materials: Lecture Outline 0:00 - Introduction 3:17 - Classes of learning problems 6:19 - Definitions 12:33 - The Q function 16:14 - Deeper into the Q function 20:49 - Deep Q Networks 26:28 - Atari results and limitations 29:53 - Policy learning algorithms 33:11 - Discrete vs continuous actions 37:22 - Training policy gradients 44:50 - RL in real life 46:02 - VISTA simulator 47:44 - AlphaGo and AlphaZero and MuZero 55:22 - 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!!