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


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