Explore deep reinforcement learning in this comprehensive lecture from MIT's Introduction to Deep Learning course. Delve into various aspects of reinforcement learning, including classes of learning problems, key definitions, and the Q function. Discover deep Q networks and their applications in Atari games, along with their limitations. Learn about policy learning algorithms, the differences between discrete and continuous actions, and how to train policy gradients. Gain insights into real-life applications of reinforcement learning, including the VISTA simulator and groundbreaking AI systems like AlphaGo, AlphaZero, and MuZero. This 58-minute lecture, delivered by Alexander Amini, provides a thorough overview of reinforcement learning concepts and their practical implementations in the field of deep learning.
Overview
Syllabus
- 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 and MuZero
- Summary
Taught by
https://www.youtube.com/@AAmini/videos
