<p>You’ve probably heard of Deepmind’s AI playing games and getting really good at playing them (like AlphaGo beating the Go world champion). Such agents are built with the help of a paradigm of machine learning called “Reinforcement Learning” (RL).</p> <p>In this course, you’ll walk through different approaches to RL. You’ll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow’s Python API. You’ll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive.<br />By the end of this course, you’ll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python.</p> <p>The code bundle for this video course is available at:&nbsp;<a href="https://github.com/PacktPublishing/-Hands-on-Reinforcement-Learning-with-TensorFlow" target="_blank">https://github.com/PacktPublishing/-Hands-on-Reinforcement-Learning-with-TensorFlow</a></p> <h1>Style and Approach</h1> <p>A practical guide that demonstrates how to create smart agents by implementing different Reinforcement Learning techniques with Python and Tensorflow, and how to easily improve their performance in different games and environments.</p>