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The Landscape of Reinforcement Learning

Humans and animals learn through a process of trial and error. This process is based on our reward mechanisms that provide a response to our behaviors. The goal of this process is to, through multiple repetitions, incentivize the repetition of actions which trigger positive responses, and disincentivize the repetition of actions which trigger negative ones. Through the trial and error mechanism, we learn to interact with the people and world around us, and pursue complex, meaningful goals, rather than immediate gratification.

Learning through interaction and experience is essential. Imagine having to learn to play football by only looking at other people playing it. If you took to the field to play a football match based on this learning experience, you would probably perform incredibly poorly.

This was demonstrated throughout the mid-20th...

Key benefits

Learn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks

Understand and develop model-free and model-based algorithms for building self-learning agents

Work with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution strategies

Description

Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents. Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods, such as the application of Q-learning and SARSA algorithms. You'll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore, you'll study the policy gradient methods, TRPO, and PPO, to improve performance and stability, before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to drive. You'll discover evolutionary strategies and black-box optimization techniques, and see how they can improve RL algorithms. Finally, you'll get to grips with exploration approaches, such as UCB and UCB1, and develop a meta-algorithm called ESBAS. By the end of the book, you'll have worked with key RL algorithms to overcome challenges in real-world applications, and be part of the RL research community.

What you will learn

Develop an agent to play CartPole using the OpenAI Gym interface

Discover the model-based reinforcement learning paradigm

Solve the Frozen Lake problem with dynamic programming

Explore Q-learning and SARSA with a view to playing a taxi game

Apply Deep Q-Networks (DQNs) to Atari games using Gym

Study policy gradient algorithms, including Actor-Critic and REINFORCE

Understand and apply PPO and TRPO in continuous locomotion environments

Get to grips with evolution strategies for solving the lunar lander problem

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

Frequently bought together

€36.99

PyTorch 1.x Reinforcement Learning Cookbook

€32.99

Reinforcement Learning Algorithms with Python

€29.99

Total € 99.97

Sebastien Feb 16, 2020

Easy to understand and in each chapter there is an example, with code, that help you understand how to actually use the algorithm

Amazon Verified review

schwarzwald Apr 12, 2020

Good content but doesn't mention that Tensorflow 1 is required. The book uses code that will not work in the current TF version. For people not intimately familiar with TF 1 vs. 2, this will be confusing.

designer Mar 16, 2023

Non of the code samples in the book and code samples in GitHub work because they are outdated. The code showed 4 years ago and the author hasn’t updated them. When I copied the samples, they all kept saying outdated and a bunch of other errors due to solfware incompatible. Wasted time and money

Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges

What do you get with Print?

Reinforcement Learning Algorithms with Python

The Landscape of Reinforcement Learning

An introduction to RL

Elements of RL

Applications of RL

Summary

Questions

Further reading

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Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges

What do you get with Print?

Key benefits

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Who is this book for?

What you will learn

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What do you get with Print?

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