Reader small image

You're reading from  Reinforcement Learning Algorithms with Python

Product typeBook
Published inOct 2019
Reading LevelBeginner
PublisherPackt
ISBN-139781789131116
Edition1st Edition
Languages
Right arrow
Author (1)
Andrea Lonza
Andrea Lonza
author image
Andrea Lonza

Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.
Read more about Andrea Lonza

Right arrow

Summary

In this chapter, we addressed the exploration-exploitation dilemma. This problem has already been tackled in previous chapters, but only in a light way, by employing simple strategies. In this chapter, we studied this dilemma in more depth, starting from the notorious multi-armed bandit problem. We saw how more sophisticated counter-based algorithms, such as UCB, can actually reach optimal performance, and with the expected logarithmic regret.

We then used exploration algorithms for AS. AS is an interesting application of exploratory algorithms, because the meta-algorithm has to choose the algorithm that best performs the task at hand. AS also has an outlet in reinforcement learning. For example, AS can be used to pick the best policy that has been trained with different algorithms from the portfolio, in order to run the next trajectory. That's also what ESBAS does...

lock icon
The rest of the page is locked
Previous PageNext Page
You have been reading a chapter from
Reinforcement Learning Algorithms with Python
Published in: Oct 2019Publisher: PacktISBN-13: 9781789131116

Author (1)

author image
Andrea Lonza

Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.
Read more about Andrea Lonza