A very simple yet serious gobang game AI based on Monte-Carlo Tree Search and implemented in pure Python

A python implementation of an agent for ultimate tic-tac-toe using Monte Carlo Tree Search and Upper Confidential Bound

A fighting game AI: KeepAwayBot, implemented in Java using Hierarchical Task Networks and the Upper Confidence Bounds Algorithm

Tools for implementing upper confidence bound optimization

Strong Aritifical Intelligence for Checkers created using Upper Confidence Tree algorithm with GUI.

The purpose of this study is to predict which ad will be the most preferred by the customers over the fictitious ads clicked by the users.

Strong Artificial Intelligence for 3-Dimensional Connect 4 using Upper Confidence Tree Algorithm

Real-Life Example for Machine Learning Projects (Python3) -Part-2

Reinforcement learning

A simple simulation of mcts tree search

A Comparative Evaluation of Active Learning Methods in Deep Recommendation

A simple implementation of Reinforcement Learning using UCB in python.

3 dimensional Four in a Row game with computer AI using Monte Carlo Tree Search (MCTS) with UCB (Upper Confidence Bounds) applied to trees (UCT in short).

Code templates for different ML algorithms

(REINFORCEMENT LEARNING) : We are given a dataset that contains information about the ads clicked by the visitors at each visit to a webpage (amongst 10 different ads). Our Task is to find the most viewed ad i.e ad having the highest distribution of the viewers in Minimum number of Rounds and Resources. Here I have used "Upper Confidence Bound" …

An AI agent implemented using Monte Carlo Tree Search (MCTS) using Upper Confidence Bounds (UCT).

Implementation of the Upper confidence bounds and Thompson sampling algorithms in R for the multi armed bandit problem