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).
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.
By the end of this course, you’ll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python.
The code bundle for this video course is available at: https://github.com/PacktPublishing/-Hands-on-Reinforcement-Learning-with-TensorFlow