资源分类
》 资源 》论文》Towards More Practical Reinforcement Learning

Towards More Practical Reinforcement Learning

2019-11-22 | |  85 |   49 |   0

Abstract Reinforcement Learning is beginning to be applied outside traditional domains such as robotics, and into human-centric domains such as healthcare and education. In these domains, two problems are critical to address: We must be able to evaluate algorithms with a collection of prior data if one is available, and we must devise algorithms that carefully trade off exploration and exploitation in such a way that they are guaranteed to converge to optimal behavior quickly, while retaining very good performance with limited data. In this thesis, I examine these two problems, with an eye towards applications to educational games

上一篇：Encoding and Combining Knowledge to Speed up Reinforcement Learning

下一篇：Unsupervised Learning of an IS-A Taxonomy from a Limited Domain-Specific Corpus

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...