资源分类
》 资源 》论文》Efficient Skill Learning using Abstraction Selection

Efficient Skill Learning using Abstraction Selection

2019-11-15 | |  59 |   43 |   0

Abstract We present an algorithm for selecting an appropriate abstraction when learning a new skill. We show empirically that it can consistently select an appropriate abstraction using very little sample data, and that it signifificantly improves skill learning performance in a reasonably large real-valued reinforcement learning domain

上一篇：Local Query Mining in a Probabilistic Prolog

下一篇：Exponential Family Sparse Coding with Applications to Self-taught Learning

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...