Hands-on Reinforcement Learning with TensorFlow

Youv'e 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. Style and Approach: 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.

Target Audience 

This course is for AI practitioners, aspiring data science professionals, and machine learning engineers who would like to learn and implement Reinforcement Learning in their applications. Basic knowledge of machine learning, TensorFlow, and Python is assumed.

Business Outcomes  

• Practical training in the Reinforcement Learning architecture for training agents
• Work with important open source Reinforcement Learning frameworks to get an in-depth knowledge of its functions
• A Production-ready approach to training Reinforcement Learning agents in Tensorflow to take on real-world projects