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Overview
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This course covers the fundamentals of reinforcement learning, including credit assignment, dynamic programming, exploration algorithms, and generalization. The teaching method includes lectures on various topics related to reinforcement learning. The course is designed for individuals interested in learning about reinforcement learning and its applications.
Syllabus
Introduction
Three Problems
Three Steps
Gameplan
Reinforcement Learning
Credit Assignment
Dynamic Programming
Exploration
Algorithm
Generalization
Algorithm Design
Sample Complexity
Roadmap
Ucbvi
Contextual Bandit
Supervised Learning
Inverse Gap Weighting
Direct Policy Search
Taught by
Simons Institute