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Fundamentals of Reinforcement Learning

The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end.By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or

4.7

109 reviews

Created by: Martha White

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Overall Score : 94 / 100

Course Description

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 introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will:- 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 problemThis course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization.

Time: 18h

Instructor Details

Martha White

4.7 Rating
109 Reviews

Martha White is an Assistant Professor in the Department of Computing Sciences at the University of Alberta, Faculty of Science. Her research focus is on developing algorithms for agents continually learning on streams of data, with an emphasis on representation learning and reinforcement learning. Martha is a PI of AMII---the Alberta Machine Intelligence Institute and a director of RLAI---the Reinforcement Learning and Artificial Intelligence Lab at the University of Alberta. She enjoys soccer, the outdoors, cooking and especially reading sci-fi.

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By Shashidhara K on 13-Nov-19

I really sorry for giving 4 star, my only reason for giving 4 star is so you can read this review. Please include some exercise on calculating the equations by hand, with solutions(this is the only reason for 4 star). Thank you for the courseCourse deserves 5 stars.(pardon my 4 stars, sorry)

By Babak B on 13-Nov-19

It provides a great introduction to RL and fundamental concepts in this area.

By Raul D M on 13-Nov-19

One of the best courses I've had on Coursera

By Antonio P on 11-Nov-19

Great introductional course on Reinforcement Learning

By Dario J G P on 10-Nov-19

Great course!

By Parsa V on 10-Nov-19

I understood all the necessary concepts of RL. I've been working on RL for some time now, but thanks to this course, now I have more basic knowledge about RL and can't wait to watch other courses

By Abhijeet A on 9-Nov-19