Contextual Bandits and the Exp4 Algorithm – Bandit Algorithms
In most bandit problems there is likely to be some additional information available at the beginning of rounds and often this information can potentially help with the action choices. For example, …
In most bandit problems there is likely to be some additional information available at the beginning of rounds and often this information can potentially help with the action choices. For example, in a web article recommendation system, where the goal is to keep the visitors engaged with the website, contextual information about the visitor of the website, the time of day, information on what is trendy, etc., can likely improve the choice of the article to be put on the “front-page”. For example, a science-oriented article is more likely to grab the attention of a science geek, and
related reading
- The Multi-Armed Bandit Problem and Its Solutions | Lil'Loglilianweng.github.io
- An Overview of Contextual Bandits | Towards Data Sciencetowardsdatascience.com
- The Bayes Banditfrancesco215.github.io
- How does in-context learning work? A framework for understanding the differences from traditional supervised learning | SAIL Blogai.stanford.edu
- Bayesian Bandits - optimizing click throughs with statistics - Chris Stucchiochrisstucchio.com
- CS 285: Lecture 13, Part 2youtube.com
- Multi-armed bandit - Wikipediaen.wikipedia.org
- CS 285: Lecture 13, Part 3youtube.com
- course_stat_rl.pdfmit.edu
- Speeding up RL with high-leverage samples | Applied Computeappliedcompute.com
- Exploration for the Efficient Deployment of Reinforcement Learning Agentsopenreview.net
- A Free Lunch from the Noise:Provable and Practical Exploration for Representation Learningarxiv.org