Course: MIT 6.S191: Introduction to Deep Learning

Lecture video: https://youtu.be/-WbN61qtTGQ

Course website: IntroToDeepLearning.com

Lecturers: Alexander Amini and Ava Soleimany

Find all my notes for this course in the ML Course Notes repo.

Please note that this is a rough draft of the notes, so you might find mistakes. The visuals and equations are directly obtained from the original slides which you can find on the course website. All of the credit goes to the lecturers. I simply hope that the notes serve as accompanying study material.

Introduction

Deep reinforcement learning (RL) shifts from the paradigm where you have a learning model that is trained on a fixed dataset.

The algorithm is now going to be placed in a dynamic environment; it’s able to explore and interact with that environment in different ways; it can try different actions and experiences to learn how to best accomplish its task in that environment without human supervision or fixed annotations from humans.

You define an objective that the algorithm should try to optimize for.

This type of algorithm, known as reinforcement learning, has many applications in the real world, ranging from robotics to gameplay.