Abstract

This thesis develops methods of task localization, task similarity discovery, and task transfer for eventual use in a reinforcement learning task library system, which can effectively “learn to learn,” improving its performance as it encounters various tasks over the lifetime of the learning system.

Degree

MS

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2005-07-07

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd901

Keywords

Reinforcement Learning, Learning to Learn, Lifelong Learning, Task Transfer, Task Libraries

Share

COinS