Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow

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Balancing CartPole

In this chapter, you will learn about the CartPole balancing problem. The CartPole is an inverted pendulum, where the pole is balanced against gravity. Traditionally, this problem is solved by control theory, using analytical equations. However, in this chapter, we will solve the problem with machine learning.

The following topics will be covered in this chapter:

Installing OpenAI Gym
The different environments of Gym

Key benefits

•Implement Q-learning and Markov models with Python and OpenAI

•Explore the power of TensorFlow to build self-learning models

•Eight AI projects to gain confidence in building self-trained applications

Description

Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.

Who is this book for?

Python Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning models. Individuals who want to work on self-learning model projects will also find this book useful.

What you will learn

•Train and evaluate neural networks built using TensorFlow for RL

•Use RL algorithms in Python and TensorFlow to solve CartPole balancing

•Create deep reinforcement learning algorithms to play Atari games

• Deploy RL algorithms using OpenAI Universe

•Develop an agent to chat with humans

•Implement basic actor-critic algorithms for continuous control

•Apply advanced deep RL algorithms to games such as Minecraft

•Autogenerate an image classifier using RL

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