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Martha White
and
Adam White
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:
Read more
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:
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/svbkm5/fundamentals
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What's inside
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.
Read more
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.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Suitable for learners completely new to Reinforcement Learning
Intermediate learners strengthen foundational understanding of RL
Teaches importance and challenges of using learning agents for decision-making
Suitable for learners working with MDPs, or those who can specify MDPs
Suited for learners interested in real-world applications of RL
Instructors have strong reputations in field of RL
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Reviews summary
Strong theoretical start to reinforcement learning
According to learners, this course provides a solid foundation in the core concepts of Reinforcement Learning. Students appreciate the clear explanations of fundamental theories like Markov Decision Processes and value functions. While the course is generally well-structured and the lectures are informative, some learners found the material, particularly dynamic programming, to be quite challenging, requiring a strong grasp of prerequisites. The programming assignments are seen as valuable for applying concepts, though some noted a need for prior coding experience. Overall, it's considered a good starting point for the specialization, albeit with a steep learning curve for newcomers.
Requires practical application of concepts.
"Implementing the epsilon-greedy agent really solidified my understanding of exploration."
"The dynamic programming assignment was tough but incredibly rewarding once completed."
"Need a decent programming background to tackle the assignments effectively without struggling."
"The hands-on coding exercises are essential for applying the theory learned."
Effectively prepares for subsequent courses.
"This course perfectly set the stage for the rest of the specialization in Reinforcement Learning."
"Now I feel much more ready to tackle the next modules on RL with a solid understanding."
"Provides essential concepts needed to progress further and understand more complex RL algorithms later."
Provides deep dive into RL fundamentals.
"I really grasped the basics of MDPs and value functions here. The explanations were thorough."
"The theoretical concepts are explained clearly, building a strong base for further study in RL."
"Found the coverage of Bellman equations particularly clear and helpful for understanding the core mechanics."
Concepts can be challenging without prior knowledge.
"Wish I had brushed up more on linear algebra and probability beforehand; they are quite necessary."
"The pace felt quite fast when covering dynamic programming; it might be overwhelming for some."
"Definitely not for complete beginners with no math or coding experience; prerequisites are real."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Fundamentals of Reinforcement Learning with these
activities:
Read "Reinforcement Learning: An Introduction, Second Edition" by Sutton and Barto
Show steps
This book is the seminal work on reinforcement learning and provides a comprehensive overview of the field. Reading this book will give students a strong theoretical foundation for the course.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
-
Read Chapter 1: Introduction
-
Read Chapter 2: Background: Markov Decision Processes
-
Work through the exercises at the end of each chapter.
Review linear algebra concepts
Show steps
Linear algebra forms the mathematical foundation for Reinforcement Learning. Reviewing these concepts will help ensure a solid understanding for when they are applied later in the course.
Browse courses on
Linear Algebra
Show steps
-
Review matrix operations, including addition, subtraction, multiplication, and determinants.
-
Practice solving systems of linear equations using Gaussian elimination.
-
Identify and classify different types of matrices, such as symmetric, orthogonal, and invertible matrices.
Mentor other students in the course
Show steps
Mentoring other students in the course is a great way to reinforce your own learning and help others to succeed.
Show steps
-
Join the course discussion forum.
-
Answer questions from other students.
-
Provide feedback on other students' work.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve reinforcement learning problems using dynamic programming
Show steps
Dynamic programming is a powerful technique for solving reinforcement learning problems. Completing these practice drills will help students develop their problem-solving skills using this technique.
Browse courses on
Dynamic programming
Show steps
-
Implement the value iteration algorithm to solve a simple gridworld problem.
-
Implement the policy iteration algorithm to solve a simple gridworld problem.
-
Compare the performance of the two algorithms on a variety of gridworld problems.
Follow the Reinforcement Learning Specialization on Coursera
Show steps
The Reinforcement Learning Specialization on Coursera provides a comprehensive overview of the field. Completing this specialization will give students a strong foundation for the course and beyond.
Browse courses on
Coursera
Show steps
-
Complete the first course in the specialization: Fundamentals of Reinforcement Learning
-
Complete the second course in the specialization: Advanced Reinforcement Learning
-
Complete the third course in the specialization: Deep Reinforcement Learning
Build a reinforcement learning agent to play a game
Show steps
Building a reinforcement learning agent to play a game is a challenging but rewarding project. Completing this project will give students the opportunity to apply the concepts they learn in the course to a real-world problem.
Browse courses on
Game AI
Show steps
-
Choose a simple game to implement, such as tic-tac-toe or Connect Four.
-
Design the state space and action space for the game.
-
Implement a reinforcement learning algorithm, such as Q-learning or SARSA, to train the agent.
-
Test the performance of the agent against a human opponent.
Create a blog post or video tutorial on a reinforcement learning topic
Show steps
Creating a blog post or video tutorial on a reinforcement learning topic will help students to solidify their understanding of the material. It will also help them to develop their communication skills.
Browse courses on
Reinforcement Learning
Show steps
-
Choose a reinforcement learning topic that you are interested in.
-
Research the topic and gather information from reliable sources.
-
Organize your thoughts and create an outline for your blog post or video tutorial.
-
Write or record your blog post or video tutorial.
-
Proofread your work and make sure it is clear and concise.
Read "Reinforcement Learning: An Introduction, Second Edition" by Sutton and Barto
Show steps
This book is the seminal work on reinforcement learning and provides a comprehensive overview of the field. Reading this book will give students a strong theoretical foundation for the course.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1: Introduction
- Read Chapter 2: Background: Markov Decision Processes
- Work through the exercises at the end of each chapter.
Review linear algebra concepts
Show steps
Linear algebra forms the mathematical foundation for Reinforcement Learning. Reviewing these concepts will help ensure a solid understanding for when they are applied later in the course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review matrix operations, including addition, subtraction, multiplication, and determinants.
- Practice solving systems of linear equations using Gaussian elimination.
- Identify and classify different types of matrices, such as symmetric, orthogonal, and invertible matrices.
Mentor other students in the course
Show steps
Mentoring other students in the course is a great way to reinforce your own learning and help others to succeed.
Show steps
Show steps
Show steps
- Join the course discussion forum.
- Answer questions from other students.
- Provide feedback on other students' work.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve reinforcement learning problems using dynamic programming
Show steps
Dynamic programming is a powerful technique for solving reinforcement learning problems. Completing these practice drills will help students develop their problem-solving skills using this technique.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Implement the value iteration algorithm to solve a simple gridworld problem.
- Implement the policy iteration algorithm to solve a simple gridworld problem.
- Compare the performance of the two algorithms on a variety of gridworld problems.
Follow the Reinforcement Learning Specialization on Coursera
Show steps
The Reinforcement Learning Specialization on Coursera provides a comprehensive overview of the field. Completing this specialization will give students a strong foundation for the course and beyond.
Browse courses on
Coursera
Show steps
Show steps
Show steps
- Complete the first course in the specialization: Fundamentals of Reinforcement Learning
- Complete the second course in the specialization: Advanced Reinforcement Learning
- Complete the third course in the specialization: Deep Reinforcement Learning
Build a reinforcement learning agent to play a game
Show steps
Building a reinforcement learning agent to play a game is a challenging but rewarding project. Completing this project will give students the opportunity to apply the concepts they learn in the course to a real-world problem.
Browse courses on
Game AI
Show steps
Show steps
Show steps
- Choose a simple game to implement, such as tic-tac-toe or Connect Four.
- Design the state space and action space for the game.
- Implement a reinforcement learning algorithm, such as Q-learning or SARSA, to train the agent.
- Test the performance of the agent against a human opponent.
Create a blog post or video tutorial on a reinforcement learning topic
Show steps
Creating a blog post or video tutorial on a reinforcement learning topic will help students to solidify their understanding of the material. It will also help them to develop their communication skills.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Choose a reinforcement learning topic that you are interested in.
- Research the topic and gather information from reliable sources.
- Organize your thoughts and create an outline for your blog post or video tutorial.
- Write or record your blog post or video tutorial.
- Proofread your work and make sure it is clear and concise.
Career center
Learners who complete Fundamentals of Reinforcement Learning will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. They work on a variety of tasks, including natural language processing, computer vision, and machine learning. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of AI that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Artificial Intelligence Engineers who want to learn more about reinforcement learning and how to apply it to their work.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning systems. They work on a variety of tasks, including data collection, feature engineering, and model training. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of machine learning that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Machine Learning Engineers who want to learn more about reinforcement learning and how to apply it to their work.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work on a variety of tasks, including requirements gathering, design, coding, and testing. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of AI that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Software Engineers who want to learn more about reinforcement learning and how to apply it to their work.
Data Scientist
Data Scientist
Data Scientists collect, analyze, and interpret data to help businesses make better decisions. They work on a variety of tasks, including data mining, statistical modeling, and machine learning. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of machine learning that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Data Scientists who want to learn more about reinforcement learning and how to apply it to their work.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to help businesses make better decisions. They work on a variety of tasks, including forecasting, optimization, and simulation. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of AI that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Operations Research Analysts who want to learn more about reinforcement learning and how to apply it to their work.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. They work on a variety of tasks, including risk assessment, portfolio optimization, and trading strategy development. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of AI that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Quantitative Analysts who want to learn more about reinforcement learning and how to apply it to their work.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work on a variety of tasks, including insurance pricing, pension plan design, and financial risk management. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of AI that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Actuaries who want to learn more about reinforcement learning and how to apply it to their work.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses make better decisions. They work on a variety of tasks, including data mining, statistical modeling, and machine learning. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of machine learning that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Statisticians who want to learn more about reinforcement learning and how to apply it to their work.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make better decisions. They work on a variety of tasks, including data mining, statistical modeling, and machine learning. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of machine learning that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Data Analysts who want to learn more about reinforcement learning and how to apply it to their work.
Business Analyst
Business Analyst
Business Analysts use data and analytics to help businesses make better decisions. They work on a variety of tasks, including process improvement, risk assessment, and financial planning. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of AI that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Business Analysts who want to learn more about reinforcement learning and how to apply it to their work.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. They work on a variety of tasks, including product design, marketing, and sales. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of AI that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Product Managers who want to learn more about reinforcement learning and how to apply it to their work.
Project Manager
Project Manager
Project Managers are responsible for the planning, execution, and delivery of projects. They work on a variety of tasks, including project planning, risk management, and stakeholder communication. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of AI that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Project Managers who want to learn more about reinforcement learning and how to apply it to their work.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics. They work on a variety of tasks, including business strategy, process improvement, and financial planning. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of AI that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Consultants who want to learn more about reinforcement learning and how to apply it to their work.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. They work on a variety of tasks, including product development, marketing, and sales. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of AI that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Entrepreneurs who want to learn more about reinforcement learning and how to apply it to their work.
Researcher
Researcher
Researchers conduct scientific research to advance our understanding of the world. They work on a variety of topics, including medicine, physics, and computer science. Reinforcement Learning Fundamentals will introduce you to the basic concepts and algorithms of reinforcement learning, which is a type of AI that allows agents to learn how to behave in an environment by trial and error. This course may be useful for Researchers who want to learn more about reinforcement learning and how to apply it to their work.
For more career information including salaries, visit:
OpenCourser.com/course/svbkm5/fundamentals
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Fundamentals of Reinforcement Learning.
Classic introduction to the field of reinforcement learning. It provides a comprehensive overview of the fundamental concepts and algorithms of RL, and is written in a clear and accessible style. This book can be considered as comprehensive and fundamental reading for this course.
Provides a comprehensive treatment of Markov decision processes (MDPs), which are a fundamental mathematical framework for modeling decision-making problems under uncertainty. This book is cited and used by this course as a reference.
Provides an introduction to the theory and practice of reinforcement learning, with a focus on the connection to optimal control. It more advanced treatment of the subject, but it is still accessible to readers with a strong foundation in mathematics.
Provides an introduction to approximate dynamic programming (ADP), which powerful technique for solving large-scale MDPs. ADP valuable tool for researchers and practitioners in a variety of fields, including operations research, machine learning, and economics.
Provides a detailed and rigorous treatment of dynamic programming, which fundamental technique for solving sequential decision-making problems. It must-read for anyone who wants to understand the theoretical foundations of RL.
Provides a comprehensive introduction to statistical learning, which powerful set of tools for understanding and predicting data. It valuable resource for anyone who wants to learn more about the statistical foundations of RL.
Provides a comprehensive introduction to machine learning, with a focus on the probabilistic approach. It valuable resource for anyone who wants to learn more about the theoretical foundations of RL.
Provides a comprehensive overview of artificial intelligence, including a chapter on RL. It valuable resource for anyone who wants to learn more about the broader field of AI.
Provides a more practical introduction to machine learning, with a focus on the use of algorithms for predictive analytics. It valuable resource for anyone who wants to learn more about the practical applications of RL.
Provides a practical guide to deep learning, with a focus on the use of fastai and PyTorch. It is another practical resource focused on deep learning.
Covers automated machine learning, and is focused on methods, systems, and challenges in that subarea.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/svbkm5/fundamentals
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Effort
4-6 hours/week
Level
Intermediate
Via
Coursera
Institutions
University of Alberta
Alberta Machine Intelligence Institute
Instructors
Martha White
Adam White
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Reinforcement Learning. It's comprised of four courses:
- Prediction and Control with Function Approximation
- Fundamentals of Reinforcement Learning (viewing)
- A Complete Reinforcement Learning System (Capstone)
- Sample-based Learning Methods
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