This article explores the implementation of a reinforcement learning algorithm called the Upper Confidence Bound (UCB) algorithm. Reinforcement learning, a subset of artificial intelligence, involves an agent interacting with an environment through a series of episodes or rounds. In each round, the agent makes a decision that may yield a reward. The agent's ultimate objective is to learn a strategy that maximizes its cumulative reward over time.

In this blog post, we’ll explore how to implement two custom object property validators using TypeScript decorators. While popular libraries like class-validator already provide a rich set of decorator-based validators, our goal here is to demonstrate how to build your own—specifically, a @Positive validator and a @NotEmpty validator—in a clean and reusable way.

I have purchased, last weekend, Paul Hoffman's book The Man Who Only Loved Numbers. This is a biography of the great and equally eccentric number theorist of all times Paul Érdös.

The book has been published in 1998, two years after the death of Érdös in Warsaw. The first 25% of Hoffman's book is interesting and entertaining at the same time. It tell the story of a man who devoted his whole life to Mathematics, who travelled from country to country carrying all his belongings in a briefcase and a bag.

Je viens d'acquérir une SlingBox. La SlingBox est un équipement que l'on connecte à un terminal de réception télé et à un routeur. La SlingBox permet par la suite de contrôler ce terminal télé à distance là où une connexion Internet est disponible et aussi d'écouter la télé à l'autre bout du monde comme si on était chez soi. La SlingBox vient en deux moèles la SlingBox 350, à 179$, et la SlingBox 500 à 299$. Le dernier modèle dispose d'une connexion sans fil et d'un port HDMI.