Advised by Prof. Jungmin So - spring '23

This repository inclues the Samurai framework and all the implemented base engines.

AI for the game "Connect Four". Available on PyPI.

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).

Tools for implementing upper confidence bound optimization

UCThello - a board game demonstrator (Othello variant) with computer AI using Monte Carlo Tree Search (MCTS) with UCB (Upper Confidence Bounds) applied to trees (UCT in short)

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

Implementation of greedy, E-greedy and Upper Confidence Bound (UCB) algorithm on the Multi-Armed-Bandit problem.

(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" …

implement basic and contextual MAB algorithms for recommendation system

The easiest way to optimize Facebook Ads using Upper Confidence Bound Algorithm. 💻

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.

A simple simulation of mcts tree search

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

Code templates for different ML algorithms

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

A Comparative Evaluation of Active Learning Methods in Deep Recommendation