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PyTorch 1.x Reinforcement Learning Cookbook

You're reading from  PyTorch 1.x Reinforcement Learning Cookbook

Product type Book
Published in Oct 2019
Publisher Packt
ISBN-13 9781838551964
Pages 340 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Profile icon Yuxi (Hayden) Liu
LinkedIn
Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.
See other products by Yuxi (Hayden) Liu

Table of Contents (11) Chapters

Preface 1. Getting Started with Reinforcement Learning and PyTorch 2. Markov Decision Processes and Dynamic Programming 3. Monte Carlo Methods for Making Numerical Estimations 4. Temporal Difference and Q-Learning 5. Solving Multi-armed Bandit Problems 6. Scaling Up Learning with Function Approximation 7. Deep Q-Networks in Action 8. Implementing Policy Gradients and Policy Optimization 9. Capstone Project – Playing Flappy Bird with DQN 10. Other Books You May Enjoy

Performing off-policy Monte Carlo control

Another MC-based approach to solve an MDP is with off-policy control, which we will discuss in this recipe.

The off-policy method optimizes the target policy, π, using data generated by another policy, called the behavior policy, b. The target policy performs exploitation all the time while the behavior policy is for exploration purposes. This means that the target policy is greedy with respect to its current Q-function, and the behavior policy generates behavior so that the target policy has data to learn from. The behavior policy can be anything as long as all actions in all states can be chosen with non-zero probabilities, which guarantees that the behavior policy can explore all possibilities.

Since we are dealing with two different policies in the off-policy method, we can only use the common steps in episodes that take place...

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Authors (1)

Profile icon Yuxi (Hayden) Liu
LinkedIn
Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.
Read more
See other products by Yuxi (Hayden) Liu

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