Using Active Relocation to Aid Reinforcement Learning

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.

• Type: Conference
• Year: 2006
• Where: FLAIRS
• Authors: Lilyana Mihalkova, Raymond J. Mooney
• Keywords: Artificial Intelligence, Effective Policy, FLAIRS 2006, Human Involvement, Reinforcement Learner