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1
Introduction
2
Three Problems
3
Three Steps
4
Gameplan
5
Reinforcement Learning
6
Credit Assignment
7
Dynamic Programming
8
Exploration
9
Algorithm
10
Generalization
11
Algorithm Design
12
Sample Complexity
13
Roadmap
14
Ucbvi
15
Contextual Bandit
16
Supervised Learning
17
Inverse Gap Weighting
18
Direct Policy Search
Description:
Explore the fundamentals of Reinforcement Learning in this comprehensive lecture from the Learning and Games Boot Camp. Delve into key concepts such as credit assignment, dynamic programming, and exploration as presented by Dylan Foster from Microsoft Research. Gain insights into algorithm design, sample complexity, and the UCB algorithm. Examine the connections between reinforcement learning, contextual bandits, and supervised learning. Investigate advanced topics like inverse gap weighting and direct policy search. Enhance your understanding of this crucial field in artificial intelligence and machine learning through this in-depth presentation from the Simons Institute.

Reinforcement Learning - Part I

Simons Institute
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