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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