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Temporal Difference, SARSA, and Q-Learning

In the previous chapter, we looked at the basics of RL. In this chapter, we will cover temporal difference (TD) learning, SARSA, and Q-learning, which were very widely used algorithms in RL before deep RL became more common. Understanding these older-generation algorithms is essential if you want to master the field, and will also lay the foundation for delving into deep RL. We will therefore spend this chapter looking at examples using these older generation algorithms. In addition, we will also code some of these algorithms using Python. We will not be using TensorFlow for this chapter, as the problems do not involve any deep neural networks under study. However, this chapter will lay the groundwork for more advanced topics that we will cover in the subsequent chapters, and will also be our first coding experience of an RL algorithm...

Key benefits

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.

Description

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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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TensorFlow Reinforcement Learning Quick Start Guide

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Colbert Philippe Nov 20, 2019

This is a fantastic book for those starting in the field.

Amazon Verified review

Praveen Narayanan Jun 27, 2019

This book presents a readable, instructive overview of the latest RL methods for the beginning practitioner. It walks the reader through the subject with motivating examples and well chosen code to get their hands dirty.

TensorFlow Reinforcement Learning Quick Start Guide: Get up and running with training and deploying intelligent, self-learning agents using Python

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TensorFlow Reinforcement Learning Quick Start Guide

Temporal Difference, SARSA, and Q-Learning

Technical requirements

Understanding TD learning

Relation between the value functions and state

Understanding SARSA and Q-Learning

Learning SARSA

Cliff walking and grid world problems

Summary

Further reading

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TensorFlow Reinforcement Learning Quick Start Guide: Get up and running with training and deploying intelligent, self-learning agents using Python

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

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Table of Contents

Recommendations for you

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