My personal implementation of the Q-Learning algorithm.

Contains exercises from Game AI CS6150 at Northeastern Univ, Boston (Spring 2017)

Implementation of Q-Learning reinforcement learning algorithm with Java programming language.

Reinforcement Learning Project

A Q Learning Model implemented in java used for MIT Battlecode 2023

用QLearning增强学习算法玩拼图游戏

CloudSim is primarily a simulation framework for modeling and simulating cloud computing infrastructures and services. While CloudSim itself does not include built-in machine learning capabilities, you can integrate machine learning techniques into CloudSim to optimize various aspects of cloud resource management.

FrozenLake - OpenAI's exercise resolved with Q-learning algorithm

Intention: should independently be able to demonstrate knowledge of the most basic methods within game AI field and able to reason around its historical development in relation to applications

A series of experiments on the performance of Q-Learning Agents in the Dots and Boxes game.

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.

A tic tac toe game in java, which can be trained by machine learning (console & gui).