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


Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!