We may earn an affiliate commission when you visit our partners.

Course Discontinued We've found some alternatives for you:

Top Active Alternatives

Practical Reinforcement Learning is no longer accepting new enrollments. We've matched the highest-rated active courses in this subject:

Udemy ★ 5.0

Ancient Chinese martial arts:Tiger Fist KungFu for beginners

Read Reviews → Enroll Active

Udemy ★ 5.0

Master Python for Data Handling [2025]

Read Reviews → Enroll Active

Udemy ★ 5.0

Beginner Mandarin Chinese: Master Travel Conversations in 3

Read Reviews → Enroll Active

Practical Reinforcement Learning

Pavel Shvechikov and Alexander Panin

This course is no longer available. Find a similar course by searching these:

reinforcement learning practical deep reinforcement learning reinforcement learning foundations state of the art rl algorithms deep neural networks for rl rl algorithms

Two deals to help you save

Save money when you learn. Here are two deals and offers that may be relevant to this course.

All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

Valid until December 2

Annual Subscription

Invest in your career growth and save. Start your annual subscription to Coursera Plus and access 10,000+ programs.

Valid until March 12, 2027

Save 40% Off 3 Months

Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Examines reinforcement learning algorithms, which is the foundation for sequential decision making tasks

Taught by Alexander Panin and Pavel Shvechikov, who are recognized for their work in reinforcement learning research and education

Develops the skills for developing and implementing reinforcement learning algorithms

Emphasizes practical applications of reinforcement learning, using concepts such as contextual bandits and seq2seq

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Practical reinforcement learning

This well-structured course uses a hands-on approach to teach the basics of Reinforcement Learning. It features practical assignments in Jupyter notebooks and a solid mix of theory in videos with links to additional materials. Despite some accent challenges, the lecturers, especially Alexander Panin, bring enthusiasm and expertise to the subject. The course is a great choice for those with a strong foundation in probability, statistics, and programming, but be prepared for some challenging assignments.

Knowledgeable instructors, especially Alexander Panin

"Loved the course, both the lecturers, especially Alexander Panin's enthusiasm kept me going and motivated for the course."

Assignments test understanding but may require extra effort

"Great course and it has challenging assignments"

Hands-on experience through practical assignments

"Practical assignments are well calibrated - the bare minimum to pass is quite easy to achieve, and more material is available for the more dedicated students."

Insufficient theoretical explanations

"The theoretical material regarding the covered methods and algorithms was generally lacking."

Difficulty understanding instructors' accents

"Instructors are difficult to understand."

"Accent is tough to understand sometimes."

Career center

Learners who complete Practical Reinforcement Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use machine learning and statistical modeling to extract insights from data. This course can help Data Scientists build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop self-driving cars, optimize supply chains, and manage financial portfolios. By taking this course, Data Scientists can learn the latest techniques in reinforcement learning and apply them to real-world problems.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design and develop machine learning models. This course can help Machine Learning Engineers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop self-driving cars, optimize supply chains, and manage financial portfolios. By taking this course, Machine Learning Engineers can learn the latest techniques in reinforcement learning and apply them to real-world problems.

See salaries and explore the career path for Machine Learning Engineer

Computer Scientist

Computer Scientists conduct research on computer science and develop new technologies. This course can help Computer Scientists build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop self-driving cars, optimize supply chains, and manage financial portfolios. By taking this course, Computer Scientists can learn the latest techniques in reinforcement learning and apply them to real-world problems.

See salaries and explore the career path for Computer Scientist

Software Engineer

Software Engineers design, develop, and maintain software systems. This course can help Software Engineers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop self-driving cars, optimize supply chains, and manage financial portfolios. By taking this course, Software Engineers can learn the latest techniques in reinforcement learning and apply them to real-world problems.

See salaries and explore the career path for Software Engineer

Mechanical Engineer

Mechanical Engineers design, develop, and test mechanical systems. This course can help Mechanical Engineers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize manufacturing processes, control robots, and develop self-driving cars. By taking this course, Mechanical Engineers can learn the latest techniques in reinforcement learning and apply them to real-world problems.

See salaries and explore the career path for Mechanical Engineer

Electrical Engineer

Electrical Engineers design, develop, and test electrical systems. This course can help Electrical Engineers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize power systems, control robots, and develop self-driving cars. By taking this course, Electrical Engineers can learn the latest techniques in reinforcement learning and apply them to real-world problems.

See salaries and explore the career path for Electrical Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can help Quantitative Analysts build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop trading strategies, optimize portfolios, and manage risk. By taking this course, Quantitative Analysts can learn the latest techniques in reinforcement learning and apply them to real-world problems.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve complex decision-making problems. This course can help Operations Research Analysts build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize supply chains, manage inventory, and schedule resources. By taking this course, Operations Research Analysts can learn the latest techniques in reinforcement learning and apply them to real-world problems.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians collect, analyze, and interpret data. This course can help Statisticians build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop predictive models, identify trends, and optimize decision-making. By taking this course, Statisticians can learn the latest techniques in reinforcement learning and apply them to real-world problems.

See salaries and explore the career path for Statistician

Business Analyst

Business Analysts analyze business processes and identify opportunities for improvement. This course may help Business Analysts build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize business processes, improve customer satisfaction, and reduce costs. By taking this course, Business Analysts can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course does not contain business or marketing related topics.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers develop and manage products. This course may help Product Managers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize product features, pricing, and marketing campaigns. By taking this course, Product Managers can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course does not contain business or marketing related topics.

See salaries and explore the career path for Product Manager

Financial Analyst

Financial Analysts analyze financial data and make investment recommendations. This course may help Financial Analysts build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop trading strategies, optimize portfolios, and manage risk. By taking this course, Financial Analysts can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course focuses primarily on deep neural networks and game playing.

See salaries and explore the career path for Financial Analyst

Consultant

Consultants provide advice and guidance to businesses and organizations. This course may help Consultants build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to optimize business processes, improve customer satisfaction, and reduce costs. By taking this course, Consultants can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course does not contain business or marketing related topics.

See salaries and explore the career path for Consultant

Data Analyst

Data Analysts collect, analyze, and interpret data. This course may help Data Analysts build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop predictive models, identify trends, and optimize decision-making. By taking this course, Data Analysts can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course places stronger emphasis on deep neural networks and game playing.

See salaries and explore the career path for Data Analyst

Market Researcher

Market Researchers conduct research on consumer behavior and market trends. This course may help Market Researchers build a foundation in reinforcement learning, which is a powerful technique for solving complex decision-making problems. Reinforcement learning can be used to develop market segmentation models, predict consumer behavior, and optimize marketing campaigns. By taking this course, Market Researchers can learn the latest techniques in reinforcement learning and apply them to real-world problems. However, the course focuses primarily on deep neural networks and game playing.

See salaries and explore the career path for Market Researcher

Reading list

We haven't picked any books for this reading list yet.

Help others find this course page by sharing it with your friends and followers:

Facebook

Copy Link

Similar courses

Here are nine courses similar to Practical Reinforcement Learning.

Project Management in Basecamp: Make a project plan

Most relevant

Bring Your Story to Life – Video Post-Production

Most relevant

AWS Security – Encryption Fundamentals

Most relevant

Learn SQL with Databricks

Most relevant

Programmatic Prompting with OpenAI: Refining and Filtering

Most relevant

Cybercrime

Most relevant

Climate Change: Carbon Capture and Storage

Most relevant

Copy.AI for Beginners: Generate texts for various use cases

Copy.AI for Beginners: Generate texts for various use...

Most relevant

MySQL for Data Analytics and Business Intelligence

Most relevant

Coursera Buyer Protection & Guarantee

Verified partner policies & purchase protections

Accredited University Partner

Free Trial & Audit

7-Day Free Trial & Free Audit

7-day free trial on monthly Coursera Plus subscriptions; free audit option available on most individual courses to explore video lectures and readings at no charge.

Return & Refund Policy

14-Day Refund Guarantee

14-day refund window on annual Coursera Plus subscriptions and single-course purchases prior to certificate issuance. Monthly subscriptions do not offer refunds once billed.

Buyer Protections

Verified Academic & Industry Partner

Accredited university and industry certifications (e.g. Stanford, Google, IBM, Yale), secure SSL payment processing, and verifiable digital certificate links.

Key Purchase Protections

14-day refund window on annual Coursera Plus and single courses

7-day free trial on monthly Coursera Plus subscriptions

Free course audit option for lectures and readings

Verified certificates from top universities & companies

OpenCourser Verified Partner Policy details last reviewed: 2026-10-22

Time

42 hours

Via

Coursera

Institutions

Higher School of Economics

National Research University Higher School of Economics

HSE University

Instructors

Pavel Shvechikov

Alexander Panin

Practical Reinforcement Learning belongs to the following collections:

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Examines reinforcement learning algorithms, which is the foundation for sequential decision making tasks

Taught by Alexander Panin and Pavel Shvechikov, who are recognized for their work in reinforcement learning research and education

Develops the skills for developing and implementing reinforcement learning algorithms

Emphasizes practical applications of reinforcement learning, using concepts such as contextual bandits and seq2seq

Top Active Alternatives

Practical Reinforcement Learning

Two deals to help you save

Traffic lights

Save this course

Reviews summary

Practical reinforcement learning

Career center

Reading list

Share

Similar courses

Coursera Buyer Protection & Guarantee

7-Day Free Trial & Free Audit

14-Day Refund Guarantee

Verified Academic & Industry Partner