Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
-
Updated
Jul 24, 2021 - Python
Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
Asynchronous Advantage Actor-Critic (A3C) algorithm for Super Mario Bros
An OpenAI Gym interface to Super Mario Bros. & Super Mario Bros. 2 (Lost Levels) on The NES
Play games without touching keyboard
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.
Random network distillation on Montezuma's Revenge and Super Mario Bros.
Reinforcement Learning for Super Mario Bros using A3C on GPU
GUI for TOAD-GAN, a PCG-ML algorithm for Token-based Super Mario Bros. Levels.
A minimal VAE trained on Super Mario Bros levels.
Reinforcement Learning PPO Super Mario Bros Agent
Several approaches using deep reinforcement learning to play Super Mario Bros.
Reinforcement learning (PPO) plays Mario.
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+).
A remake of the Super Mario Bros game written in Python using Pygame
A Reinforcement Learning agent that learns to play Super Mario Bros.
Deep-Reinforcement-Learning with A3C in 'Super Mario Bros' (NES, 1985)
A primeira fase do jogo Super Mario Bros
Add a description, image, and links to the super-mario-bros topic page so that developers can more easily learn about it.
To associate your repository with the super-mario-bros topic, visit your repo's landing page and select "manage topics."