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Charles Ivan Niswander II
In this project-based course, we will explore Reinforcement Learning in Python. Reinforcement Learning, or RL for short, is different from supervised learning methods in that, rather than being given correct examples by humans, the AI finds the correct...
Read more
In this project-based course, we will explore Reinforcement Learning in Python. Reinforcement Learning, or RL for short, is different from supervised learning methods in that, rather than being given correct examples by humans, the AI finds the correct...
Read more
In this project-based course, we will explore Reinforcement Learning in Python. Reinforcement Learning, or RL for short, is different from supervised learning methods in that, rather than being given correct examples by humans, the AI finds the correct answers for itself through a predefined framework of reward signals.
In this course, we will discuss theories and concepts that are integral to RL, such as the Multi-Arm Bandit problem and its implications, and how Markov Decision processes can be leveraged to find solutions. Then we will implement code examples in Python of basic Temporal Difference algorithms and Monte Carlo techniques. Finally, we implement an example of Q-learning in Python.
I would encourage learners to experiment with the tools and methods discussed in this course. The learner is highly encouraged to experiment beyond the scope of the course.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/9uoty0/introduction
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Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Engages learners with hands-on exercises and interactive learning
Guides learners from the foundational concepts of Reinforcement Learning
Emphasizes practical implementation of RL techniques using Python
Introduces advanced Reinforcement Learning algorithms like Q-learning
Suitable for learners with a background in machine learning and programming
Working knowledge of Python is assumed
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Reviews summary
Rl for beginners in python
This course provides a basic overview of Reinforcement Learning (RL). It covers the theory and concepts behind RL, such as Markov Decision Processes and Temporal Difference algorithms. Students will also implement code examples in Python. Note that this course is best suited for learners who are based in North America.
Covers basic RL concepts
"Suitable for basic understanding of concepts of Reinforcement Learning."
Rhyme tool is difficult to use
"The Rhyme tool by Coursera is absolutely pathetic with it automatically closing the workspace (where we can type the code) every few seconds."
"Using IDLE is a pain, please use a modern IDE like a IPython notebook or VS Code."
Instructor lacks teaching experience
"The instructor doesn't seem to properly know the topics he's talking about."
"The tutor is not consistent, not engaging, does stupid jokes and shows memes instead of course material."
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 Introduction to Reinforcement Learning in Python with these
activities:
Review Reinforcement Learning: An Introduction
Show steps
This book will provide a conceptual understanding of Reinforcement Learning and its applications.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
-
Read the introduction to the book.
-
Read the first three chapters of the book.
-
Summarize the main concepts of each chapter in your own words.
Follow Reinforcement Learning tutorials
Show steps
This activity will help you to learn Reinforcement Learning by following step-by-step tutorials.
Browse courses on
Reinforcement Learning
Show steps
-
Find a set of Reinforcement Learning tutorials.
-
Follow the tutorials step-by-step.
-
Implement the algorithms in Python.
Create a simple Reinforcement Learning agent
Show steps
This project will allow you to apply the concepts of Reinforcement Learning in a practical setting.
Browse courses on
Reinforcement Learning
Show steps
-
Choose a simple Reinforcement Learning algorithm, such as Q-learning.
-
Implement the algorithm in Python.
-
Test the agent on a simple environment, such as the CartPole environment.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Reinforcement Learning algorithms
Show steps
This activity will help you to develop your programming skills and your understanding of Reinforcement Learning algorithms.
Browse courses on
Reinforcement Learning
Show steps
-
Choose a set of Reinforcement Learning algorithms to practice.
-
Implement the algorithms in Python.
-
Test the algorithms on a variety of environments.
Create a presentation about Reinforcement Learning
Show steps
This activity will help you to develop your communication skills and your understanding of Reinforcement Learning.
Browse courses on
Reinforcement Learning
Show steps
-
Choose a topic related to Reinforcement Learning.
-
Create a presentation that explains the topic in a clear and concise way.
-
Present your presentation to a group of people.
Attend Reinforcement Learning meetups
Show steps
This activity will help you to connect with other Reinforcement Learning practitioners and learn about the latest developments in the field.
Browse courses on
Reinforcement Learning
Show steps
-
Find a Reinforcement Learning meetup in your area.
-
Attend the meetup and meet other Reinforcement Learning practitioners.
-
Exchange ideas and learn about the latest developments in Reinforcement Learning.
Write a blog post about Reinforcement Learning
Show steps
This activity will help you to solidify your understanding of Reinforcement Learning by explaining it to others.
Browse courses on
Reinforcement Learning
Show steps
-
Choose a topic related to Reinforcement Learning.
-
Write a blog post that explains the topic in a clear and concise way.
-
Publish your blog post online.
Attend Reinforcement Learning workshops
Show steps
This activity will help you to deepen your understanding of Reinforcement Learning by learning from experts in the field.
Browse courses on
Reinforcement Learning
Show steps
-
Find a Reinforcement Learning workshop that is relevant to your interests.
-
Attend the workshop and learn from the experts.
-
Implement the techniques that you learn in the workshop.
Review Reinforcement Learning: An Introduction
Show steps
This book will provide a conceptual understanding of Reinforcement Learning and its applications.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
Show steps
Show steps
- Read the introduction to the book.
- Read the first three chapters of the book.
- Summarize the main concepts of each chapter in your own words.
Follow Reinforcement Learning tutorials
Show steps
This activity will help you to learn Reinforcement Learning by following step-by-step tutorials.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Find a set of Reinforcement Learning tutorials.
- Follow the tutorials step-by-step.
- Implement the algorithms in Python.
Create a simple Reinforcement Learning agent
Show steps
This project will allow you to apply the concepts of Reinforcement Learning in a practical setting.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Choose a simple Reinforcement Learning algorithm, such as Q-learning.
- Implement the algorithm in Python.
- Test the agent on a simple environment, such as the CartPole environment.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Reinforcement Learning algorithms
Show steps
This activity will help you to develop your programming skills and your understanding of Reinforcement Learning algorithms.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Choose a set of Reinforcement Learning algorithms to practice.
- Implement the algorithms in Python.
- Test the algorithms on a variety of environments.
Create a presentation about Reinforcement Learning
Show steps
This activity will help you to develop your communication skills and your understanding of Reinforcement Learning.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Choose a topic related to Reinforcement Learning.
- Create a presentation that explains the topic in a clear and concise way.
- Present your presentation to a group of people.
Attend Reinforcement Learning meetups
Show steps
This activity will help you to connect with other Reinforcement Learning practitioners and learn about the latest developments in the field.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Find a Reinforcement Learning meetup in your area.
- Attend the meetup and meet other Reinforcement Learning practitioners.
- Exchange ideas and learn about the latest developments in Reinforcement Learning.
Write a blog post about Reinforcement Learning
Show steps
This activity will help you to solidify your understanding of Reinforcement Learning by explaining it to others.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Choose a topic related to Reinforcement Learning.
- Write a blog post that explains the topic in a clear and concise way.
- Publish your blog post online.
Attend Reinforcement Learning workshops
Show steps
This activity will help you to deepen your understanding of Reinforcement Learning by learning from experts in the field.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Find a Reinforcement Learning workshop that is relevant to your interests.
- Attend the workshop and learn from the experts.
- Implement the techniques that you learn in the workshop.
Career center
Learners who complete Introduction to Reinforcement Learning in Python will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers develop new and improved artificial intelligence algorithms. This course would be of great use to those who want to specialize in reinforcement learning.
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers design, develop, and test self-driving cars. This course may be particularly useful for those who want to specialize in developing or improving reinforcement learning models for autonomous vehicles.
Data Scientist
Data Scientist
Data Scientists use their understanding of data to extract meaningful information. This often includes building and applying machine learning models. This course would be particularly useful to those who want to specialize in building or improving Reinforcement Learning models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning applications. These applications can range from speech and facial recognition to predictive analytics for businesses. This course may be useful for those who want to specialize in building or improving Reinforcement Learning models.
Game Designer
Game Designer
Game Designers create and develop video games. This course may be useful for those who want to include reinforcement learning in their games, creating new and innovative gameplay experiences.
Robotics Engineer
Robotics Engineer
Robotics Engineers combine their knowledge of mechanical engineering and computer science to design, build, and maintain robots. This course may be helpful in building a foundation for applying reinforcement learning to robotic control.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, engineering, and medicine. This course may be useful for those who want to conduct research in reinforcement learning.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course may be useful in building a foundation for using reinforcement learning to improve business operations.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. This course may be useful for those who want to specialize in using reinforcement learning to improve business decision-making.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and mitigate risks. This course may be useful for those who want to specialize in using reinforcement learning to improve risk management.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be helpful for those who want to specialize in using reinforcement learning to improve product development and launch.
Industrial Engineer
Industrial Engineer
Industrial Engineers optimize the efficiency of production processes. This course may be helpful in building a foundation for using reinforcement learning to improve manufacturing processes.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts apply mathematical and statistical models to financial data. This course may be helpful in building a foundation for developing reinforcement learning models for algorithmic trading.
Software Engineer
Software Engineer
Software Engineers apply their knowledge of designing and developing software to build and maintain computer applications. While a Computer Science degree is typically required, this course may be helpful in building a foundation for developing reinforcement learning applications for a business.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment recommendations. This course may be helpful for those who want to specialize in using reinforcement learning to improve investment performance.
For more career information including salaries, visit:
OpenCourser.com/course/9uoty0/introduction
Reading list
We've selected 12 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
Introduction to Reinforcement Learning in Python.
Classic in the field of reinforcement learning and provides a comprehensive overview of the subject. It valuable reference for anyone who wants to learn more about reinforcement learning.
Provides a comprehensive overview of reinforcement learning. It great resource for anyone who wants to learn about the fundamentals of reinforcement learning.
Provides a comprehensive overview of machine learning. It great resource for anyone who wants to learn about the fundamentals of machine learning.
Provides a comprehensive overview of deep reinforcement learning. It great resource for anyone who wants to learn about the latest advances in reinforcement learning.
Provides a comprehensive overview of artificial intelligence. It great resource for anyone who wants to learn about the fundamentals of artificial intelligence.
Provides a comprehensive overview of probability and statistics. It great resource for anyone who wants to learn about the fundamentals of probability and statistics.
Provides a comprehensive overview of artificial intelligence. It great resource for anyone who wants to learn about the fundamentals of artificial intelligence.
Provides a comprehensive overview of probability and statistics. It great resource for anyone who wants to learn about the fundamentals of probability and statistics.
Provides a comprehensive overview of linear algebra. It great resource for anyone who wants to learn about the fundamentals of linear algebra.
Provides a comprehensive overview of calculus. It great resource for anyone who wants to learn about the fundamentals of calculus.
Provides a comprehensive overview of linear algebra. It great resource for anyone who wants to learn about the fundamentals of linear algebra.
Provides a comprehensive overview of calculus. It great resource for anyone who wants to learn about the fundamentals of calculus.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9uoty0/introduction
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Charles Ivan Niswander II
Good to know
Engages learners with hands-on exercises and interactive learning
Guides learners from the foundational concepts of Reinforcement Learning
Emphasizes practical implementation of RL techniques using Python
Introduces advanced Reinforcement Learning algorithms like Q-learning
Suitable for learners with a background in machine learning and programming
Working knowledge of Python is assumed
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