Interpretable, scalable and flexible AI for video games

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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.