The aim of this project is to achieve maximum success by the algorithm in the rock-paper-scissors game with the reinforcement learning.

Generates two clash-free class schedules using a genetic algorithm with random selection, crossover, and mutation. Designed for 3 teachers over 3 days and 3 periods.

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.

This program uses Python's random module to generate random integers and make random choices. It generates random values in different ranges and simulates a coin flip and dice roll, showcasing the randint() and choice() functions.

This repository contains the source code of research paper titled: "A Generalized Quantum Algorithm for Assuring Fairness in Random Selection Among 2ⁿ Participants."

Selects a Random Wishlisted Game from your Steam Wishlist.

Implementation of Q-Learning and Double Q-Learning for optimal pathfinding in large, dynamic environments. Uses reward shaping and adaptive exploration. Compares RL performance with Dijkstra and random selection, showing Q-Learning's scalability and superior cumulative rewards.