Using Active Relocation to Aid Reinforcement Learning

Lilyana S. Mihalkova, Raymond J. Mooney

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.

Subjects: 12. Machine Learning and Discovery; 12.1 Reinforcement Learning

Submitted: Feb 10, 2006


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