Interpretable, scalable and flexible AI for video games

我建议引入自然语言处理（NLP）技术，并继续努力开发高效的基于模型的强化学习代理，这是QMUL目前正在研究的主题。已经出版了几部作品来介绍自然语言强化学习代理。对于该主题的最近综述，参见（Luketina等人，2019）以及最近使用自然语言来概括强化学习环境的动态的应用程序（Zhong，Rocktaeschel和Grefenstette，2019）。近年来，基于模型的RL越来越重要。一个非常有趣的方法是由（Berkenkamp，Turchetta，Schoellig和Krause，2017）提出的。作者提出了一种利用环境动态的统计属性来确保代理安全收集数据的方法。我相信我可以在视频游戏AI中广泛应用这种技术，以在不确定的情况下建模和鼓励安全行为。

I propose to introduce Natural Language Processing (NLP) techniques and continue the effort to develop efficient model-based reinforcement learning agents, a topic that is currently investigated at QMUL. Several works have already been published to introduce Natural Language informed Reinforcement Learning agents. For a recent review of the topic see (Luketina et al., 2019) and for a recent application that uses Natural Language to generalise the dynamic of a Reinforcement Learning environment (Zhong, Rocktaeschel, & Grefenstette, 2019). Model-based RL is gaining increasing importance in recent years. One very interesting approach is presented by (Berkenkamp, Turchetta, Schoellig, & Krause, 2017). The authors present an approach that leverages the statistical properties of the dynamics of the environment to ensure that the agent safely collects data. I believe that I can apply this technique extensively in video games AI to model and encourage safe behaviour under uncertainty situations.