We have previously explored two multi-armed bandit (MAB) strategies: Maximum Average Reward (MAR) and Upper Confidence Bound (UCB). Both approaches rely on the observed average reward to determine which arm to pull next, using a deterministic scoring mechanism for decision-making.

In this article, I will explore the balance between exploration and exploitation, a key concept in reinforcement learning and optimization problems. To illustrate this, I will use the multi-armed bandit problem as an example. I will also explain how the epsilon-greedy strategy effectively manages this balance.

In previous blog posts, we explored the multi-armed bandit (MAB) problem and discussed the Upper Confidence Bound (UCB) algorithm as one approach to solving it. Research literature has introduced multiple algorithms for tackling this problem, and there is always room for experimenting with new ideas. To facilitate the implementation and comparison of different algorithms, we introduce a framework for MAB solvers.

This article focuses on evaluating the implementation of the Upper Confidence Bound (UCB) algorithm discussed herein. The evaluation is conducted using a single dataset provided by Super Data Science.

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.