GAIG Game AI Research Group @ QMUL

Neural Game Engine: Accurate learning of generalizable forward models from pixels


Abstract

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 generalize 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: https://github.com/Bam4d/Neural-Game-Engine.
URL: https://ieeexplore.ieee.org/document/9231688
Github: https://github.com/Bam4d/Neural-Game-Engine 

Cite this work

@inproceedings{bamford2020neural,
author= {Bamford, Chris and Lucas, Simon M},
title= {{Neural Game Engine: Accurate learning of generalizable forward models from pixels}},
year= {2020},
booktitle= {{IEEE Conference on Games (CoG)}},
pages= {81--88},
url= {https://ieeexplore.ieee.org/document/9231688},
abstract= {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 generalize 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: https://github.com/Bam4d/Neural-Game-Engine.},
}

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