Popular GitHub Repositories for Super Mario Bros

Proximal Policy Optimization (PPO) algorithm for Super Mario Bros

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

Official repository for "TOAD-GAN: Coherent Style Level Generation from a Single Example" by Maren Awiszus, Frederik Schubert and Bodo Rosenhahn.

This program evolves an AI using the NEAT algorithm to play Super Mario Bros.

An implementation of (Double/Dueling) Deep-Q Learning to play Super Mario Bros.

Random network distillation on Montezuma's Revenge and Super Mario Bros.

Reinforcement Learning for Super Mario Bros using A3C on GPU

My implementation of a reinforcement learning model using Stable-Baselines3 to play the NES Super Mario Bros.

A Reinforcement Learning agent that learns to play Super Mario Bros.

Reinforcement Learning PPO Super Mario Bros Agent

Several approaches using deep reinforcement learning to play Super Mario Bros.

Super Mario Bros. (NES) gameplay dataset for machine learning.

Durham University, Dissertation: 1st - 92. Additional Materials and Codebase for the paper: Combining Recent Advances in Reinforcement Learning for Super Mario Bros. - Recurrent Replay Deeper Denser Distributed DQN+ (R2D4+).

Reinforcement learning (PPO) plays Mario.

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.

MarioPPO implementation uses the TensorFlow machine learning platform

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

Playing Super Mario Bros using PPO