TensorFlow Reinforcement Learning Quick Start Guide

Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving.
The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator.
By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems.

Type
ebook
Category
Data
publication date
2019-03-30
what you will learn

Understand the theory and concepts behind modern Reinforcement Learning algorithms
Code state-of-the-art Reinforcement Learning algorithms with discrete or continuous actions
Develop Reinforcement Learning algorithms and apply them to training agents to play computer games
Explore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlow
Use A3C to play CartPole and LunarLander
Train an agent to drive a car autonomously in a simulator

no of pages
184
duration
368
key features
Explore efficient Reinforcement Learning algorithms and code them using TensorFlow and Python * Train Reinforcement Learning agents for problems, ranging from computer games to autonomous driving. * Formulate and devise selective algorithms and techniques in your applications in no time. *
approach
An easy to follow guide to understand Reinforcement Learning algorithms, code them in Python and TensorFlow, train and test RL agents for several control problems.
audience
Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.
meta description
Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks
short description
This book is an essential guide for anyone interested in Reinforcement Learning. The book provides an actionable reference for Reinforcement Learning algorithms and their applications using TensorFlow and Python. It will help readers leverage the power of algorithms such as Deep Q-Network (DQN), Deep Deterministic Policy Gradients (DDPG), and Proximal Policy Optimization (PPO) to solve challenging control and decision-making problems.
subtitle
Get up and running with training and deploying intelligent, self-learning agents using Python
keywords
DQN, DDPG, PPO, Reinforcement Learning agents, TensorFlow agents, modern Reinforcement Learning algorithms
Product ISBN
9781789533583