作者: Hao Wang , Yang Gao , Xingguo Chen
DOI:
关键词: Transfer of learning 、 Reinforcement learning 、 Learning classifier system 、 Semi-supervised learning 、 Machine learning 、 Multi-task learning 、 Temporal difference learning 、 Unsupervised learning 、 Artificial intelligence 、 Instance-based learning 、 Engineering
摘要: This paper studies the problem of transfer learning in context reinforcement learning. We propose a novel method that can speed up learn- ing with aid previously learnt tasks. Before performing extensive episodes, our attempts to analyze task via some exploration environment, and then reuse previous experience whenever it is possible appropriate. In particular, proposed consists four stages: 1) subgoal discovery, 2) option construc- tion, 3) similarity searching, 4) reusing. Especially, order fulfill identifying similar options, we measure between which built upon intuition options have state- action probabilities. examine algorithm using experiments, comparing existing methods. The results show outperforms conventional non-transfer algorithms, as well methods, by wide margin.