World Models is a framework described by
David Ha and Jürgen Schmidhuber: https://arxiv.org/abs/1803.10122. The framework aims to train an AI agent that can perform well in virtual gaming environments. World Models consists of three main components: Vision (V), Model (M), and Controller (C).
As part of my MSc Artificial Intelligence dissertation at the University of Edinburgh, I implemented World Models from the ground up in Chainer. My implementation was picked up by Chainer and tweeted:
World Models (https://t.co/wlQDRRJiBn) implementation in Chainer is now available!https://t.co/sMfAyiBFTj— Chainer (@ChainerOfficial) July 5, 2018
David Ha himself also tweeted it:
A reimplementation of our paper from scratch using Chainer! Unlike other reimplementation attempts, he also reproduced the generative environment part, using much less compute. He also describes clearly Mixture Density Networks, CMA-ES, and implemented those from scratch as well. https://t.co/EzfrdH7cKL— hardmaru (@hardmaru) July 5, 2018
The full implementation and more details can be found at: https://github.com/AdeelMufti/WorldModels.