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Reinforcement Learning Maze Solver

Reinforcement learning is an exciting subfield of machine learning that focuses on teaching agents how to make decisions based on rewards and penalties. Reinforcement learning is like training a puppy. Just as a puppy learns by receiving a reward when it behaves well and being scolded when it misbehaves, reinforcement learning algorithms learn in a similar fashion as they attempt to solve a problem.

Our new course, Reinforcement Learning Maze Solver, teaches you to harness the power of reinforcement learning by guiding you through the process of building a simple algorithm that trains a learning agent to solve a 2D maze in the fewest possible steps.

Whether you’re interested in artificial intelligence and machine learning or just looking to expand your Python programming skills, this course is an excellent choice.


Course prerequisites and the project you’ll build

This practice-oriented course is aimed at learners who are already familiar with Python and want to learn how to implement another simple algorithm. You will need a good understanding of Python syntax, data structures, classes, and objects, as well as a fair understanding of NumPy arrays and operations on them. Basic math skills and the ability to read and understand formulas are also required.

In this course, you will:

✅ Learn key concepts of reinforcement learning.

✅ Discover the types of use cases for these algorithms.

✅ Understand the limitations of these approaches.

By the end of this course, you will have implemented a Q-learning algorithm that uses rewards and penalties to teach a learning agent how to navigate through a maze, iteratively updating a table with scores to optimize the learning process. In addition, you will build a dynamic visualization of the agent’s progress through the maze, which will help you see whether the algorithm you built works properly.


If you are new to Python or machine learning, we recommend starting with our Introduction to Python and NumPy courses. This will give you a solid foundation to build upon as you progress to more advanced topics.In this reinforcement learning project, you will be solving a predefined maze. If you want a more comprehensive experience, we recommend taking our AMazing course, which guides you through the process of building the same maze from scratch.

Meet the course author

Sofia Kolchanova has a background in bioinformatics and comparative genomics, having studied and worked in both the USA and Russia. She holds a Master’s degree in genetics and biotechnology. In 2018, Sofia began working at JetBrains as a collaborator for the Neurodevelopment and Neurophysiology Lab. Later in 2020, she transitioned to JetBrains Academy, where she currently works as a data analyst and educational content creator. Sofia is an enthusiast of science, technology, and critical thinking and a lifelong learner, passionate about the team’s goal of bridging the gap between complex STEM concepts and accessible, engaging content.

What’s next

From teaching robots to perform complex tasks to creating intelligent game-playing agents, reinforcement learning has become an essential tool in the field of AI.

By completing Reinforcement Learning Maze Solver, you’ll gain valuable hands-on experience implementing a simple reinforcement learning algorithm. From here, you can take your knowledge further by exploring more advanced Python algorithms and applications of reinforcement learning or by delving deeper into machine learning and artificial intelligence.

If you have any questions or would like to share your feedback, feel free to leave a comment below or contact us at academy@jetbrains.com.

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Your JetBrains Academy team

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