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Deep Reinforcement Learning with Python

You're reading from   Deep Reinforcement Learning with Python Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

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Product type Paperback
Published in Sep 2020
Publisher Packt
ISBN-13 9781839210686
Length 760 pages
Edition 2nd Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Toc

Table of Contents (22) Chapters Close

Preface 1. Fundamentals of Reinforcement Learning 2. A Guide to the Gym Toolkit FREE CHAPTER 3. The Bellman Equation and Dynamic Programming 4. Monte Carlo Methods 5. Understanding Temporal Difference Learning 6. Case Study – The MAB Problem 7. Deep Learning Foundations 8. A Primer on TensorFlow 9. Deep Q Network and Its Variants 10. Policy Gradient Method 11. Actor-Critic Methods – A2C and A3C 12. Learning DDPG, TD3, and SAC 13. TRPO, PPO, and ACKTR Methods 14. Distributional Reinforcement Learning 15. Imitation Learning and Inverse RL 16. Deep Reinforcement Learning with Stable Baselines 17. Reinforcement Learning Frontiers 18. Other Books You May Enjoy
19. Index
Appendix 1 – Reinforcement Learning Algorithms 1. Appendix 2 – Assessments

Actor-Critic Methods – A2C and A3C

So far, we have covered two types of methods for learning the optimal policy. One is the value-based method, and the other is the policy-based method. In the value-based method, we use the Q function to extract the optimal policy. In the policy-based method, we compute the optimal policy without using the Q function.

In this chapter, we will learn about another interesting method called the actor-critic method for finding the optimal policy. The actor-critic method makes use of both the value-based and policy-based methods. We will begin the chapter by understanding what the actor-critic method is and how it makes use of value-based and policy-based methods. We will acquire a basic understanding of actor-critic methods, and then we will learn about them in detail.

Moving on, we will also learn how actor-critic differs from the policy gradient with baseline method, and we will learn the algorithm of the actor-critic method in...

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