Reinforcement Learning
Offered By: Alexander Amini via YouTube
Course Description
Overview
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.
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
Tags
Related Courses
Computational NeuroscienceUniversity of Washington via Coursera Reinforcement Learning
Brown University via Udacity Reinforcement Learning
Indian Institute of Technology Madras via Swayam FA17: Machine Learning
Georgia Institute of Technology via edX Introduction to Reinforcement Learning
Higher School of Economics via Coursera