资源分类
》 资源 》论文》Encoding and Combining Knowledge to Speed up Reinforcement Learning

Encoding and Combining Knowledge to Speed up Reinforcement Learning

2019-11-22 | |  60 |   47 |   0

Abstract Reinforcement learning algorithms typically require too many ‘trial-and-error’ experiences before reaching a desirable behaviour. A considerable amount of ongoing research is focused on speeding up this learning process by using external knowledge. We contribute in several ways, proposing novel approaches to transfer learning and learning from demonstration, as well as an ensemble approach to combine knowledge from various sources

上一篇：Inverse Reinforcement Learning in Relational Domains

下一篇：Towards More Practical Reinforcement 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...