Top 7 best bandit algorithms: Which is the best one in 2019?
When you want to find bandit algorithms, you may need to consider between many choices. Finding the best bandit algorithms is not an easy task. In this post, we create a very short list about top 7 the best bandit algorithms for you. You can check detail product features, product specifications and also our voting for each product. Let’s start with following top 7 bandit algorithms:
Best bandit algorithms
![By John Myles White Bandit Algorithms for Website Optimization (1st First Edition) [Paperback]](https://images-na.ssl-images-amazon.com/images/I/51XvxxIwdPL.jpg)
1. Bandit Algorithms for Website Optimization: Developing, Deploying, and Debugging
Feature
Used Book in Good ConditionDescription
When looking for ways to improve your website, how do you decide which changes to make? And which changes to keep? This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site. Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success.
This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. Youll quickly learn the benefits of several simple algorithmsincluding the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithmsby working through code examples written in Python, which you can easily adapt for deployment on your own website.
- Learn the basics of A/B testingand recognize when its better to use bandit algorithms
- Develop a unit testing framework for debugging bandit algorithms
- Get additional code examples written in Julia, Ruby, and JavaScript with supplemental online materials
2. Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
Description
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
3. Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)
Feature
Bradford BookDescription
Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
4. By John Myles White Bandit Algorithms for Website Optimization (1st First Edition) [Paperback]
5. Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014, Proceedings (Lecture Notes in Computer Science)
Description
This book constitutes the proceedings of the 25th International Conference on Algorithmic Learning Theory, ALT 2014, held in Bled, Slovenia, in October 2014, and co-located with the 17th International Conference on Discovery Science, DS 2014. The 21 papers presented in this volume were carefully reviewed and selected from 50 submissions. In addition the book contains 4 full papers summarizing the invited talks. The papers are organized in topical sections named: inductive inference; exact learning from queries; reinforcement learning; online learning and learning with bandit information; statistical learning theory; privacy, clustering, MDL, and Kolmogorov complexity.6. Bandit Algorithms for Website Optimization by White (2013-01-03)
7. Bandits
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