- Machine Learning
- Markov Model
- Artificial Intelligence
- Reinforcement Learning
- Algorithms
- Probability Distribution
- Linear Algebra
Fundamentals of Reinforcement Learning
Completed by Tianna Seitz
July 3, 2024
15 hours (approximately)
Tianna Seitz'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
