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

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Product Details

Publication date : Oct 18, 2019

Length 366 pages

Edition : 1st Edition

Language : English

ISBN-13 : 9781789131116

Category :

Data

Languages :

Python (Beginner)

Concepts :

Reinforcement Learning

19 Chapters

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

1. Section 1: Algorithms and Environments

Section 1: Algorithms and Environments

2. The Landscape of Reinforcement Learning

The Landscape of Reinforcement Learning

An introduction to RL

3. Implementing RL Cycle and OpenAI Gym

Implementing RL Cycle and OpenAI Gym

Setting up the environment

OpenAI Gym and RL cycles

Development of ML models using TensorFlow

Introducing TensorBoard

Types of RL environments

Summary

Questions

Further reading

4. Solving Problems with Dynamic Programming

Solving Problems with Dynamic Programming

MDP

Categorizing RL algorithms

5. Section 2: Model-Free RL Algorithms

Section 2: Model-Free RL Algorithms

6. Q-Learning and SARSA Applications

Q-Learning and SARSA Applications

Learning without a model

TD learning

SARSA

Applying SARSA to Taxi-v2

Q-learning

Applying Q-learning to Taxi-v2

Summary

Questions

7. Deep Q-Network

Deep Q-Network

Deep neural networks and Q-learning

DQN

8. Learning Stochastic and PG Optimization

Learning Stochastic and PG Optimization

Policy gradient methods

Understanding the REINFORCE algorithm

REINFORCE with baseline

Learning the AC algorithm

Summary

Questions

Further reading

9. TRPO and PPO Implementation

TRPO and PPO Implementation

Roboschool

Natural policy gradient

Trust region policy optimization

Proximal Policy Optimization

Summary

Questions

Further reading

10. DDPG and TD3 Applications

DDPG and TD3 Applications

Combining policy gradient optimization with Q-learning

Deep deterministic policy gradient

Twin delayed deep deterministic policy gradient (TD3)

Summary

Questions

Further reading

11. Section 3: Beyond Model-Free Algorithms and Improvements

Section 3: Beyond Model-Free Algorithms and Improvements

12. Model-Based RL

Model-Based RL

Model-based methods

Combining model-based with model-free learning

ME-TRPO applied to an inverted pendulum

Summary

Questions

Further reading

13. Imitation Learning with the DAgger Algorithm

Imitation Learning with the DAgger Algorithm

Technical requirements

The imitation approach

Playing Flappy Bird

Understanding the dataset aggregation algorithm

IRL

Summary

Questions

Further reading

14. Understanding Black-Box Optimization Algorithms

Understanding Black-Box Optimization Algorithms

Beyond RL

The core of EAs

Scalable evolution strategies

Applying scalable ES to LunarLander

Summary

Questions

Further reading

15. Developing the ESBAS Algorithm

Developing the ESBAS Algorithm

Exploration versus exploitation

Approaches to exploration

Epochal stochastic bandit algorithm selection

Summary

Questions

Further reading

16. Practical Implementation for Resolving RL Challenges

Practical Implementation for Resolving RL Challenges

Best practices of deep RL

Challenges in deep RL

Advanced techniques

RL in the real world

Future of RL and its impact on society

17. Assessments

18. Other Books You May Enjoy

Other Books You May Enjoy

Leave a review - let other readers know what you think

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Authors (1)

Andrea Lonza

Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.

See other products by Andrea Lonza

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

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