MIT 6.S191 (2021): Reinforcement Learning

MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini
January 2021

For all lectures, slides, and lab materials: http://introtodeeplearning.com

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


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