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

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

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

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Deep Reinforcement Learning

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Deep Reinforcement Learning

In this Deep Reinforcement Learning, you'll learn: Introduction; Classes of learning problems; Definitions; The Q function; Deeper into the Q function; Deep Q Networks; Atari results and limitations; Policy learning algorithms; Discrete vs continuous actions;Training policy gradients; RL in real life; VISTA simulator; AlphaGo and AlphaZero

3.95 GEEK