  Access to a fast and easily copied forward model of a game is essential for
model-based reinforcement learning and for algorithms such as Monte Carlo tree
search, and is also beneficial as a source of unlimited experience data for
model-free algorithms. Learning forward models is an interesting and important
challenge in order to address problems where a model is not available. Building
upon previous work on the Neural GPU, this paper introduces the Neural Game
Engine, as a way to learn models directly from pixels. The learned models are
able to generalise to different size game levels to the ones they were trained
on without loss of accuracy. Results on 10 deterministic General Video Game AI
games demonstrate competitive performance, with many of the games models being
learned perfectly both in terms of pixel predictions and reward predictions.
The pre-trained models are available through the OpenAI Gym interface and are
available publicly for future research here:
\url{https://github.com/Bam4d/Neural-Game-Engine}
