MIT 6.S191 (2024): Reinforcement Learning
MIT Introduction to Deep Learning 6.S191: Lecture 5 Deep Reinforcement Learning Lecturer: Alexander Amini 2024 Edition For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline: 0:00 - Introduction 2:20 - Classes of learning problems 6:33 - Definitions 12:30 - The Q function 17:29 - Deeper into the Q function 23:12 - Deep Q Networks 30:36 - Atari results and limitations 34:24 - Policy learning algorithms 39:31 - Discrete vs continuous actions 43:21 - Training policy gradients 49:10 - RL in real life 51:33 - VISTA simulator 53:24 - AlphaGo and AlphaZero and MuZero 58:58 - 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!!
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