Balakrishnan K. TensorFlow Reinforcement Learning Quick Start Guide

Packt Publishing, 2019. — 184 p. — ISBN: 1789533589.Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasksKey FeaturesExplore 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.Book DescriptionAdvances 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.