- Algorithms
- Markov Model
- Probability Distribution
- Machine Learning
- Linear Algebra
- Artificial Intelligence
- Reinforcement Learning
Fundamentals of Reinforcement Learning
Completed by Shengyuan Wang
November 8, 2021
15 hours (approximately)
Shengyuan Wang's account is verified. Coursera certifies their successful completion of Fundamentals of Reinforcement Learning
What you will learn
Formalize problems as Markov Decision Processes
Understand basic exploration methods and the exploration / exploitation tradeoff
Understand value functions, as a general-purpose tool for optimal decision-making
Know how to implement dynamic programming as an efficient solution approach to an industrial control problem
Skills you will gain
