基本信息
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Bio
My research primarily involves studying domain adaptatiion and generalization for applications of reinforcement learning (RL) in games and robotics. We investigate techniques to bridge domain gaps when applying control policies learned over different distributions of domains/tasks.
I am broadly interested in researching techniques and tools that exploit the versatility of data generated in simulations to develop more robust AI, with tentative applications in robotics, creative tools, mixed-reality, and game development. I am especially interested in utilizing video games and game engines for improved robustness in simulated environments, with the goal of constructing sandbox environments and training schemes which in turn allow for the development of more robust machine learning (ML) polcies.
Research Interests
Papers共 10 篇Author StatisticsCo-AuthorSimilar Experts
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ArXiv (2021)
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2021 IEEE Conference on Games (CoG)pp.01-08, (2021)
semanticscholar(2019)
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semanticscholar(2019)
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user-5bd69975530c70d56f390249(2018)
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