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.

This project implements the classic Snake game with a reinforcement learning agent that learns how to play using a Replay Q-Network (RQN) and Linear Q-Learning. The agent is trained through trial and error by receiving rewards for beneficial actions and penalties for harmful actions

This repository contains implementations of various reinforcement learning algorithms, including Q-Learning, Deep Q Networks (DQN), Policy Gradient methods, and more. Explore, learn, and apply these algorithms to solve challenging problems in AI and machine learning.

Deep Q-Networks (DQN) to train an AI agent to play the Snake game. The AI controls the snake, making decisions in real-time to maximize its score while avoiding collisions. The agent learns to improve its performance by playing multiple games and adjusting its strategy based on rewards and penalties.

• Master Thesis • In this project we aim to create a virtual environment of a 2DOF robot learning handwriting via reinforcement learning.

Welcome to the Lunar Lander project! This repository contains the implementation of a lunar landing simulation using machine learning and reinforcement learning techniques.

University assignments and sandbox projects connected to machine learning study.

The AI solves a maze using Q Learning

Replication of Algorithmic collusion in Duopoly and Oligopoly models

Collection of Machine Learning Algorithms

Automated analysis of hyperparameter configurations for n-step Q-Learning reinforcement agents

C++ implementation of the Q-Learning algorithm applied to a simple grid world problem

Implementation of RL in the cloud for energy minimization due to migration and excess power consumption.

Créer un jeu vidéo (Pong) avec Python et Machine Learning | Pygame | Q-learnning | Apprentissage supervisé

Candy crush with ML models solvers and companions.

Trading Bot using Double Deep Reinfocement Learning

The following project concerns the development of an intelligent agent for the famous game produced by Nintendo Super Mario Bros. More in detail: the goal of this project was to design, implement and train an agent with the Q-learning reinforcement learning algorithm.

Dieses Projekt implementiert ein Q-Learning-Modell in C#. Es trainiert ein Modell basierend auf definierten Zuständen und Aktionen und speichert die Belohnungen pro Episode in einer JSON-Datei. Zusätzlich wird ein Python-Skript verwendet, um die Ergebnisse zu visualisieren.

Playable Tic Tac Toe game against AI Q-Learning agent.