Deep Reinforcement Learning in VizDoom

Student: Sopov Vitalii

Educational Programme: Data Science (Master)

Year of Graduation: 2021

Transformers is a novel neural network architecture that is successfully used in natural language processing tasks and is starting to be used in other areas such as video processing and image processing. However, transformers are not yet actively explored in reinforcement learning scenarios. In this work we combine transformer architectures with reinforcement learning and train them in VizDoom game environment, producing agents that play better in comparison to traditional neural network architectures.

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