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Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788834247
Length 546 pages
Edition 1st Edition
Languages
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Author (1):
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Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
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Table of Contents (21) Chapters Close

Preface 1. What is Reinforcement Learning? FREE CHAPTER 2. OpenAI Gym 3. Deep Learning with PyTorch 4. The Cross-Entropy Method 5. Tabular Learning and the Bellman Equation 6. Deep Q-Networks 7. DQN Extensions 8. Stocks Trading Using RL 9. Policy Gradients – An Alternative 10. The Actor-Critic Method 11. Asynchronous Advantage Actor-Critic 12. Chatbots Training with RL 13. Web Navigation 14. Continuous Action Space 15. Trust Regions – TRPO, PPO, and ACKTR 16. Black-Box Optimization in RL 17. Beyond Model-Free – Imagination 18. AlphaGo Zero Other Books You May Enjoy Index

Variance reduction

In the previous chapter, we briefly mentioned that one of the ways to improve the stability of PG methods is to reduce the variance of the gradient. Now let's try to understand why this is important and what it means to reduce the variance. In statistics, variance is the expected square deviation of a random variable from the expected value of this variable.

Variance reduction

Variance shows us how far values are dispersed from the mean. When variance is high, the random variable can take values deviated widely from the mean. On the following plot, there is a normal (Gaussian) distribution with the same value of mean Variance reduction, but with different values for the variance.

Variance reduction

Figure 1: The effect of variance on Gaussian distribution

Now let's return to PG. It has already been stated in the previous chapter, that the method's idea is to increase the probability of good actions and decrease the chance of bad ones. In math notation, our PG was written as Variance reduction. The scaling factor Q(s, a) specifies...

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