So far, we have learned about how reinforcement learning (RL) works. In the upcoming chapters, we will learn about Deep reinforcement learning (DRL), which is a combination of deep learning and RL. DRL is creating a lot of buzz around the RL community and is making a serious impact on solving many RL tasks. To understand DRL, we need to have a strong foundation in deep learning. Deep learning is actually a subset of machine learning and it is all about neural networks. Deep learning has been around for a decade, but the reason it is so popular right now is because of the computational advancements and availability of a huge volume of data. With this huge volume of data, deep learning algorithms will outperform all classic machine learning algorithms. Therefore, in this chapter, we will learn about several deep learning algorithms like recurrent neural...
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Hands-On Reinforcement Learning with Python
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Hands-On Reinforcement Learning with Python
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Overview of this book
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.
The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.
By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Table of Contents (16 chapters)
Preface
Free Chapter
Introduction to Reinforcement Learning
Getting Started with OpenAI and TensorFlow
The Markov Decision Process and Dynamic Programming
Gaming with Monte Carlo Methods
Temporal Difference Learning
Multi-Armed Bandit Problem
Deep Learning Fundamentals
Atari Games with Deep Q Network
Playing Doom with a Deep Recurrent Q Network
The Asynchronous Advantage Actor Critic Network
Policy Gradients and Optimization
Capstone Project – Car Racing Using DQN
Recent Advancements and Next Steps
Assessments
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