Using Active Relocation to Aid Reinforcement Learning (2006)
We propose a new framework for aiding a reinforcement learner by allowing it to relocate, or move, to a state it selects so as to decrease the number of steps it needs to take in order to develop an effective policy. The framework requires a minimal amount of human involvement or expertise and assumes a cost for each relocation. Several methods for taking advantage of the ability to relocate are proposed, and their effectiveness is tested in two commonly-used domains.
In Prodeedings of the 19th International FLAIRS Conference (FLAIRS-2006), pp. 580-585, Melbourne Beach, FL, May 2006.

Lilyana Mihalkova Ph.D. Alumni lilymihal [at] gmail com
Raymond J. Mooney Faculty mooney [at] cs utexas edu