Foundations of Reinforcement Learning | Princeton University
Princeton University
Comprehensive introduction to the fundamentals of reinforcement learning, including Markov decision processes, dynamic programming, and temporal-difference learning.
University CoursesMachine LearningPythonTensorFlow
Introduction
This course provides a comprehensive introduction to the foundations of reinforcement learning, a powerful machine learning technique for sequential decision-making problems. Taught by experts at Princeton University, the lectures cover the core concepts, algorithms, and applications of reinforcement learning.
Highlights
Comprehensive coverage of reinforcement learning fundamentals, including Markov decision processes, dynamic programming, and temporal-difference learning
Hands-on programming assignments and projects using popular reinforcement learning libraries
Insights into the latest advancements and cutting-edge research in the field
Opportunity to learn from renowned experts in the field of reinforcement learning
Recommendation
This course is highly recommended for students, researchers, and professionals interested in exploring the powerful capabilities of reinforcement learning. It provides a solid foundation for those seeking to apply reinforcement learning techniques to solve complex, sequential decision-making problems in various domains, such as robotics, game AI, and resource optimization.
YouTube Videos
How GetVM Works
Learn by Doing from Your Browser Sidebar
Access from Browser Sidebar
Simply install the browser extension and click to launch GetVM directly from your sidebar.
Select Your Playground
Choose your OS, IDE, or app from our playground library and launch it instantly.
Learn and Practice Side-by-Side
Practice within the VM while following tutorials or videos side-by-side. Save your work with Pro for easy continuity.