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Table of Contents
The Landscape of Reinforcement Learning
Implementing RL Cycle and OpenAI Gym
Solving Problems with Dynamic Programming
Q learning and SARSA Applications
Deep Q-Network
Learning Stochastic and DDPG optimization
TRPO and PPO implementation
DDPG and TD3 Applications
Model-Based RL
Imitation Learning with the DAgger Algorithm
Understanding Black-Box Optimization Algorithms
Developing the ESBAS Algorithm
Practical Implementation for Resolving RL Challenges

Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries

Key Features
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
Book 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
Who this book is for
If you are an AI researcher, deep learning user, or anyone who wants to learn reinforcement learning from scratch, this book is for you. You'll also find this reinforcement learning book useful if you want to learn about the advancements in the field. Working knowledge of Python is necessary.