MIT 6.S191 (2020): Reinforcement Learning

MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini
January 2020

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

Lecture Outline
0:00 – Introduction
2:47 – Classes of learning problems
4:59 – Definitions
9:23 – The Q function
13:18 – Deeper into the Q function
17:17 – Deep Q Networks
21:44 – Atari results and limitations
24:13 – Policy learning algorithms
27:36 – Discrete vs continuous actions
30:11 – Training policy gradients
36:04 – RL in real life
37:40 – VISTA simulator
38:55 – AlphaGo and AlphaZero
42:51 – Summary


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