MIT 6.S191 (2021): Reinforcement Learning

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

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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As someone who is studying in the field of neuromodulation, it’s quite hard to wrap my head around these tough topics. This course taught by MIT helps me to better understand these difficult topics. Thanks a lot!

rickknoben

In less than 60mins, it is very comprehensive and NOT boring.

KensonLeung

Love the marriage of Reinforcement Learning with Deep Learning and Deep Learning exploring and interacting with the environment! Yet another masterpiece Alexander. Thank you

ajaytaneja

Quite engaging and intellectually stimulating. Thanks Alex for the explicit analysis of the Deep RL algorithm

josephozone

What a pedagogically terrific lecture. From the high level explanation, visualization to the sublet knowledge, and state-of-the-art. Happily watching!

DuongNguyen-fswi

Thank you for such an amazing course! For the seemingly unintuitive optimal solution of the Atari game, I think it is due to the constraints of human that we may not be able to move fast and accurate enough to catch the ball when it speeds up once it breaks through the corners, which is not a problem at all for the computer. To verify this hypothesis, we can lower the accuracy of catching the ball and see how the optimal strategy changes with it.

seasnowcai

Awesome video Alexander, Reinforcement Learning is yet, another great and important step in Deep Learning. The set of applications in which Reinforcement can be applied it's amazing! I really liked your explanation about the Q Values using Atari Breakout, it's so good! Thanks for this new class! I'm learning a lot with this course, it's surely complimenting my studies into Deep Learning!

reandov

Thanks, Alexander for this great lecture! I can't wait till the next Friday for the next lecture!

carletonai

The whole presentation is so clean and engaging. Thank you for the awesome introduction to RL🙂.

nitinprasad

so much joy by watching these videos. feel like the lecturer has a great personality. plz keep uploading. make education is equal for every one

Rleiy

Awesome lecture, learned a lot about RL in just 57 minutes. Thanks for making it so effortless to learn such a difficult and complex topic.

rasheedkhan

Great intro video for reinforcement learning. Thank you so much!

Sopiro

Thanks a lot for this lecture! You are a great instructor. Please keep uploading :)

Majagarbulinska

What a great great great lecture!! Thank you for making this public!!

MuriloBoaretoDelefrate

I haven't come across any other material on the topic that's as lucid as this. Thanks a lot for the exposition.
A natural question: is this field's advance going to depend on the development of simulators for real-world situations? Can you please provide some details on how you developed the simulator for autonomous vehicles?

nintishia