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  • Provider Coursera
  • Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 4 weeks long
  • Learn more about MOOCs

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Overview

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 problem

This 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.

Syllabus

Welcome to the Course!
-Welcome to: Fundamentals of Reinforcement Learning, the first course in a four-part specialization on Reinforcement Learning brought to you by the University of Alberta, Onlea, and Coursera. In this pre-course module, you'll be introduced to your instructors, get a flavour of what the course has in store for you, and be given an in-depth roadmap to help make your journey through this specialization as smooth as possible.

The K-Armed Bandit Problem
-For the first week of this course, you will learn how to understand the exploration-exploitation trade-off in sequential decision-making, implement incremental algorithms for estimating action-values, and compare the strengths and weaknesses to different algorithms for exploration. For this week’s graded assessment, you will implement and test an epsilon-greedy agent.

Markov Decision Processes
-When you’re presented with a problem in industry, the first and most important step is to translate that problem into a Markov Decision Process (MDP). The quality of your solution depends heavily on how well you do this translation. This week, you will learn the definition of MDPs, you will understand goal-directed behavior and how this can be obtained from maximizing scalar rewards, and you will also understand the difference between episodic and continuing tasks. For this week’s graded assessment, you will create three example tasks of your own that fit into the MDP framework.

Value Functions & Bellman Equations
-Once the problem is formulated as an MDP, finding the optimal policy is more efficient when using value functions. This week, you will learn the definition of policies and value functions, as well as Bellman equations, which is the key technology that all of our algorithms will use.

Dynamic Programming
-This week, you will learn how to compute value functions and optimal policies, assuming you have the MDP model. You will implement dynamic programming to compute value functions and optimal policies and understand the utility of dynamic programming for industrial applications and problems. Further, you will learn about Generalized Policy Iteration as a common template for constructing algorithms that maximize reward. For this week’s graded assessment, you will implement an efficient dynamic programming agent in a simulated industrial control problem.

Taught by

Martha White and Adam White

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Reviews for Coursera's Fundamentals of Reinforcement Learning
5.0 Based on 7 reviews

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  • 1
Luiz C
5.0 a month ago
Luiz completed this course, spending 2 hours a week on it and found the course difficulty to be easy.
Fantastic Course. That's the RL MOOC I have been waiting for so long. No surprise it is from Students of RL guru R. Sutton at Uni of Alberta. Very clearly and simply explained. Exercise and Test difficulty spot on. Wouldn't change a iota from this Course. Can't wait to do the rest of this RL specialization
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Stewart A
5.0 2 weeks ago
Stewart completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
This is a great course on Reinforcement Learning (RL) and I thoroughly recommend it. This is the first course in the four course Reinforcement Learning specialization from the Alberta Machine Intelligence Institute (AMII) at University of Alberta. The course introduces the key concepts and goals of RL and follows the standard text on the subject, (Sutton & Barto 2018), very closely. AMII is the "home" of Rich Sutton and Andy Barto the authors of Reinforcement Learning an Introduction which is used throughout the specialization. It is available as a free PDF as part of the course material and e…
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Anonymous
5.0 2 weeks ago
Anonymous completed this course.
I enjoyed taking this course and feel that it has expanded my tool-kit. The course was well constructed with reading assignments from the book followed by relatively short videos and assignments. I spent around 4-5 hours a week and most of the time was on the reading assignment. I enjoyed the programming assignments though it would have been nice if they had provided lesser scaffolding and asked us to write more code. The quizzes were ok and some of the questions were rather ambiguous and could have been improved with better wording. Overall I had a great learning experience and look forward to completing the series.
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Anonymous
5.0 3 weeks ago
Anonymous completed this course.
It is a reallui good course. It is basically an introduction course to RL but it has good reference (that you have to read) and video lectures which explain the reference book with some examples. The resources, such as notebooks, are well done and challenging enough.
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Anonymous
5.0 2 weeks ago
Anonymous completed this course.
This course enforces one to become strong with the fundamentals of RL and implementing it in code just adds icing on the cake by giving confidence. I would recommend one to take this course and move ahead in this field.
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Kim F
5.0 4 weeks ago
Kim completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
I found the course very interesting. The videos very good an informative. I like the fact that the videos are describing theory (with real world examples) and not trying to teach Python while doing it.
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Anonymous
5.0 3 weeks ago
Anonymous completed this course.
This course is a great introduction to the theory of finding optimal solutions to MDP problems and felt like it provided a solid grounding for learning about RL.
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