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MIT 6.S191: Reinforcement Learning

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

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