Using relative novelty to identify useful temporal abstractions in reinforcement learning

Özgür Şimşek, Andrew G. Barto

Research output: Chapter in Book/Report/Conference proceedingChapter

103 Citations (Scopus)

Abstract

We present a new method for automatically creating useful temporal abstractions in reinforcement learning. We argue that states that allow the agent to transition to a different region of the state space are useful subgoals, and propose a method for identifying them using the concept of relative novelty. When such a state is identified, a temporally-extended activity (e.g., an option) is generated that takes the agent efficiently to this state. We illustrate the utility of the method in a number of tasks.
Original languageEnglish
Title of host publicationProceedings of the Twenty-first International Conference on Machine Learning (ICML 2004): Banff, Alberta, Canada, July 4-8, 2004
EditorsCarla E. Brodley
PublisherAssociation for Computing Machinery
Volume69
Publication statusPublished - 2004

Publication series

NameACM International Conference Proceeding Series
PublisherACM

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