MIT: Reinforcement Learning

Explore deep reinforcement learning in this comprehensive lecture from MIT's Introduction to Deep Learning course. Delve into key concepts of reinforcement learning, including dynamic environments, Q-functions, and policy gradients. Learn about deep Q networks, their advantages and limitations, and the differences between discrete and continuous action spaces. Discover real-life applications of reinforcement learning, including its role in mastering the game of Go. Gain insights from case studies and practical examples presented by lecturer Alexander Amini. Perfect for those seeking to understand the fundamentals and advanced topics in deep reinforcement learning.

Syllabus

Intro
Learning in Dynamic Environments
Classes of Learning Problems
Reinforcement Learning (RL): Key Concepts
Defining the Q-function
Deep Reinforcement Learning Algorithms
Digging deeper into the Q-function
Deep Q Network Summary
Downsides of Q-learning
Discrete vs Continuous Action Spaces
Policy Gradient (PG): Key Idea
Training Policy Gradients: Case Study
Reinforcement Learning in Real Life
Reinforcement Learning and the Game of Go
Deep Reinforcement Learning Summary