论文标题
神经游戏引擎：准确地从像素中学习可推广的远期模型
Neural Game Engine: Accurate learning of generalizable forward models from pixels
论文作者
论文摘要
访问游戏的快速且易于复制的前向模型对于基于模型的强化学习和诸如Monte Carlo Tree搜索之类的算法至关重要，并且作为无限制体验数据的来源，也很有益。学习前进模型是一个有趣而重要的挑战，以解决无法可用的模型的问题。本文基于神经GPU的先前工作，介绍了神经游戏引擎，以直接从像素中学习模型。博学的模型能够将其概括到不同尺寸的游戏水平，而不会损失准确性。 10个确定性的一般视频游戏AI游戏的结果表明了竞争性能，许多游戏模型在像素预测和奖励预测方面都可以完美地学习。预先训练的型号可通过OpenAI Gym界面获得，可在此处公开可用：\ url {https://github.com/bam4d/neural-game-engine}
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}
