Local Forward Model Learning for Sample-Efficient Sequential Decision Making in Open World 3D Games

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Video games have been a platform for state of the art machine learning algorithms for some time now. Having a custom virtual environment provides us with the possibility to develop more and more complex algorithms to tackle practical life problems one step at a time. The proposed study will use MALMO as a platform to make sample efficient learning models and implement statistical forward planning models in a complex 3D partially observable game like Minecraft. An efficient learned local model, combined with Statistical Forward Planning methods that have been successful in various 2D games would allow the transition to the 3D environment which would breach the current barrier and bring us one step closer to the goal of creating general artificial intelligence.