Watch Practical Reinforcement Learning - Agents and Environments

Practical Reinforcement Learning - Agents and Environments is an educational series from Packt Publishing that explores the fascinating field of reinforcement learning. Over the course of the series, viewers will learn the fundamentals of how machines learn through trial-and-error, and will gain practical experience building their own reinforcement learning agents using the popular Python programming language.

The series is hosted by an experienced instructor who guides viewers through the foundational concepts that make reinforcement learning possible. These concepts include formal definitions of agents, environments, rewards, policies, and values, as well as algorithms that govern how agents behave as they interact with their environments.

The instructor also explains how reinforcement learning differs from other forms of machine learning, and gives examples of real-world problems that can be solved using reinforcement learning techniques. These examples include game playing, robotic control, and optimization problems.

Throughout the series, viewers will get hands-on experience building their own reinforcement learning agents using Python and the popular reinforcement learning library TensorFlow. They will start with simple problems, such as navigating a grid-world environment, and gradually work up to more complex problems, such as training an agent to play a simple game like Pong or Space Invaders.

In addition to hands-on coding exercises, the series includes interactive quizzes and assignments that reinforce key concepts and help viewers gauge their understanding of the material. There are also several case studies that showcase how reinforcement learning has been used to solve real-world problems in industries like finance, energy, and healthcare.

Other topics covered in the series include deep reinforcement learning, which combines traditional reinforcement learning with neural networks to solve even more complex problems, and model-based and model-free reinforcement learning, which differ in the way they represent and learn from their environment.

By the end of the series, viewers will have a deep understanding of the fundamental concepts of reinforcement learning, as well as practical experience building their own reinforcement learning agents using Python and TensorFlow. They will also be able to apply this knowledge to solve real-world problems in industries like finance, energy, and healthcare.

Overall, Practical Reinforcement Learning - Agents and Environments is an invaluable resource for anyone interested in machine learning or artificial intelligence. The series is well-structured, informative, and engaging, and provides viewers with a solid foundation for further study and exploration in this exciting field.