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Learn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks
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Understand and develop model-free and model-based algorithms for building self-learning agents
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Work with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution strategies
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.
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.
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Develop an agent to play CartPole using the OpenAI Gym interface
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Discover the model-based reinforcement learning paradigm
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Solve the Frozen Lake problem with dynamic programming
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Explore Q-learning and SARSA with a view to playing a taxi game
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Apply Deep Q-Networks (DQNs) to Atari games using Gym
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Study policy gradient algorithms, including Actor-Critic and REINFORCE
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Understand and apply PPO and TRPO in continuous locomotion environments
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Get to grips with evolution strategies for solving the lunar lander problem
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