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This repository explores the application of three reinforcement learning algorithms—Deep Q-Networks (DQN), Double Deep Q-Networks (DDQN), and Proximal Policy Optimization (PPO)—for playing Super Mario Bros using the OpenAI Gym and nes-py emulator. It includes a comparative analysis of these models.

Asynchronous Advantage Actor-Critic (A3C) algorithm for Super Mario Bros

Reinforcement learning algorithm that blends the N-th order Markov property with abstract MDPs, PPO, and a hybrid model-free/model-based approach.

A reinforcement learning project designed to learn and complete the original Super Mario Bros.

SuperMario with the boys

A Simple Super Mario Bros Game with JavaScript & HTML5 Canvas

GUI for TOAD-GAN, a PCG-ML algorithm for Token-based Super Mario Bros. Levels.

A primeira fase do jogo Super Mario Bros

Recreation of the classic Super Mario Bros game using the Phaser framework, featuring random level generation.

Special NES emulator for playing Super Mario Bros with text-mode graphics in a terminal.

A way to interact with the Twitch Chat API in order to write a TAS with everyone's favourite governmental system, democracy!

Développement d'une application de jeu de Super Mario Bros en C++ - Qt – Graphics - Multimédia.

A super mario bros clone.

An open source reimplementation of Super Mario Bros. 2 written in Lua and LÖVE.

Super Mario bros game using Rust Piston game engine

A platform game based on Dark Blue from Eloquent JavaScript

AI Python Framework for Super Mario Bros.

This project aims to utilize reinforcement learning (RL) techniques to train an artificial intelligence agent capable of playing the iconic Super Mario game.