Sofia Kolchanova has a background in bioinformatics and comparative genomics, having studied and worked in both the USA and Russia. 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. 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 are new to Python or machine learning, we recommend starting with our Introduction to Python and NumPy courses. 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. ✅ Understand the limitations of these approaches.īy 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. ✅ Discover the types of use cases for these algorithms. ✅ Learn key concepts of reinforcement learning. Basic math skills and the ability to read and understand formulas are also required. 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. This practice-oriented course is aimed at learners who are already familiar with Python and want to learn how to implement another simple algorithm.
GET STARTED Course prerequisites and the project you’ll build 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. 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. 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. Reinforcement learning is like training a puppy. Reinforcement learning is an exciting subfield of machine learning that focuses on teaching agents how to make decisions based on rewards and penalties.
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