Please share your thoughts on this book with others by leaving a review on the site that you bought it from. If you purchased the book from Amazon, please leave us an honest review on this book's Amazon page. This is vital so that other potential readers can see and use your unbiased opinion to make purchasing decisions, we can understand what our customers think about our products, and our authors can see your feedback on the title that they have worked with Packt to create. It will only take a few minutes of your time, but is valuable to other potential customers, our authors, and Packt. Thank you!
Hands-On Reinforcement Learning with Python
By :
Hands-On Reinforcement Learning with Python
By:
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
Other Books You May Enjoy
How would like to rate this book
Customer Reviews
