[arXiv:2601.06820] Offical implement of Bgolearn

A novel parallel UCT algorithm with linear speedup and negligible performance loss.

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

Offline evaluation of multi-armed bandit algorithms

Optimizing the best Ads using Reinforcement learning Algorithms such as Thompson Sampling and Upper Confidence Bound.

We implemented a Monte Carlo Tree Search (MCTS) from scratch and we successfully applied it to Tic-Tac-Toe game.

该仓库包含基于 PyWebIO 的 UCB（上置信界）算法 在线演示，UCB 算法常用于多臂老虎机问题，以优化决策并最大化累积奖励。演示包括自动 UCB 算法模拟和交互式手动策略对比。

Repository of Online Learning algorithms, including Bandits, UCB, and more.

This repo contains code templates of all the machine learning algorithms that are used, like Regression, Classification, Clustering, etc.

Checking CTR(Click Thorugh Rate) of an ad using Thompson Sampling (Reinforcement Lrearning)

Using SciKit Learn few Deep Learning Rules and Algorithms are implemented

Code for the paper "Truncated LinUCB for Stochastic Linear Bandits"

This project implements an ad optimization system using a hybrid approach combining Thompson Sampling and Upper Confidence Bound (UCB) algorithms. The system learns to select the most effective ads based on user context and historical performance.

Reinforcement learning used in the game of pong

LoRa@FIIT algorithms comparison using jupyter notebooks

Implementing a UCB1-style algorithm adapted for selecting the top-6 books simultaneously

A collection of games accompanied by a generalised Monte Carlo Tree Search Artificial Intelligence in combination with Upper Confidence Bounds.

Predicting the best Ad from the given Ads.

A highly efficient implementation of the Monte Carlo Tree Search algorithm on an example game.