Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making.
This course is part of the Reinforcement Learning Specialization
Offered By
About this Course
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
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
- Artificial Intelligence (AI)
- Machine Learning
- Reinforcement Learning
- Function Approximation
- Intelligent Systems
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Syllabus - What you will learn from this course
Welcome to the Course!
An Introduction to Sequential Decision-Making
Markov Decision Processes
Value Functions & Bellman Equations
Dynamic Programming
Reviews
- 5 stars81.99%
- 4 stars14.61%
- 3 stars2.36%
- 2 stars0.35%
- 1 star0.66%
TOP REVIEWS FROM FUNDAMENTALS OF REINFORCEMENT LEARNING
This is a relatively gentle introduction for the mathematically sophisticated, but does well to set the stage for the rest of the specialization and introduce the newcomer to the field.
An excellent introduction to Reinforcement Learning, accompanied by a well-organized & informative handbook. I definitely recommend this course to have a strong foundation in Reinforcement Learning.
Clear instruction and insightful exercises! Enjoy this course! Also, please read the book if you want to understand better about the course materials and rationales behind the exercises.
The book is essential reading. It took me longer than the estimates to do the reading and the programming assignments. I would have liked more gridworld examples to get a faster hang of it.
About the Reinforcement Learning Specialization

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