MIT 6.S191: Introduction to Deep Learning - MIT 6.S191 (2023): Reinforcement Learning

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deep learning mit artificial intelligence neural networks machine learning 6s191 6.s191 mit deep learning ava soleimany soleimany alexander amini amini

Full Transcript

MIT Introduction to Deep Learning 6.S191: Lecture 5 Deep Reinforcement Learning Lecturer: Alexander Amini 2023 Edition For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline: 0:00 - Introduction 3:49 - Classes of learning problems 6:48 - Definitions 12:24 - The Q function 17:06 - Deeper into the Q function 21:32 - Deep Q Networks 29:15 - Atari results and limitations 32:42 - Policy learning algorithms 36:42 - Discrete vs continuous actions 39:48 - Training policy gradients 47:17 - RL in real life 49:55 - VISTA simulator 52:04 - AlphaGo and AlphaZero and MuZero 56:34 - 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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