Explore deep reinforcement learning in this comprehensive 57-minute lecture from MIT's Introduction to Deep Learning course. Delve into key concepts including classes of learning problems, Q functions, Deep Q Networks, policy learning algorithms, and the differences between discrete and continuous actions. Examine real-world applications of reinforcement learning, including the VISTA simulator and breakthrough AI systems like AlphaGo, AlphaZero, and MuZero. Gain insights into Atari game results, limitations of current approaches, and techniques for training policy gradients. Perfect for those seeking a thorough understanding of reinforcement learning principles and their practical implementations in artificial intelligence.
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
