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We all learn by interacting with the world around us, constantly experimenting and interpreting the results. Reinforcement learning is a machine learning technique that follows this same explore-and-learn approach. Ideally suited to improve applications like automatic controls, simulations, and other adaptive systems, a RL algorithm takes in data from its environment and improves its accuracy based on the positive and negative outcomes of these interactions. This liveVideo course will get you started!

about the subject

With reinforcement learning, an AI agent learns from its environment, constantly responding to the feedback it gets. The agent optimizes its behavior to avoid negative consequences and enhance positive outcomes. The resulting algorithms are always looking for the most positive and efficient outcomes!

Importantly, with reinforcement learning you don’t need a mountain of data to get started. You just let your AI agent poke and prod its environment, which makes it much easier to take on novel research projects without well-defined training datasets.

about the video

Reinforcement Learning in Motion introduces you to the exciting world of machine systems that learn from their environments! Developer, data scientist, and expert instructor Phil Tabor guides you from the basics all the way to programming your own constantly-learning AI agents. In this course, he’ll break down key concepts like how RL systems learn, how to sense and process environmental data, and how to build and train AI agents. As you learn, you’ll master the core algorithms and get to grips with tools like Open AI Gym, numpy, and Matplotlib.

Reinforcement systems learn by doing, and so will you in this interactive, hands-on course! You’ll build and train a variety of algorithms as you go, each with a specific purpose in mind. The rich and interesting examples include simulations that train a robot to escape a maze, help a mountain car get up a steep hill, and balance a pole on a sliding cart. You’ll even teach your agents how to navigate Windy Gridworld, a standard exercise for finding the optimal path even with special conditions!


You’ll need to be familiar with Python and machine learning basics. Examples use Python libraries like NumPy and Matplotlib. You'll also need some understanding of linear algebra and calculus, please see the equations in the Free Downloads section for examples.

about the instructor

Phil Tabor is a lifelong coder with a passion for simplifying and teaching complex topics. A physics PhD and former Intel process engineer, he works as a data scientist, teaches machine learning on YouTube, and contributes to Sensenet, an open source project using deep reinforcement learning to teach robots to identify objects by touch.

A neat introduction to dive into Deep Reinforcement Learning.

Sandeep Chigurupati

After watching the first few sections you'll be able to experiment with some simple algorithms and definitely want to continue learning more.

Rob Pacheco

Gives a fantastic look into the examples and mathematical background.

Harald Kuhn

It prepares you to apply reinforcement learning directly to a problem you have in hand!

Yaser Marey
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