Performance Guarantees for Homomorphisms beyond Markov Decision Processes

  • Sultan Javed Majeed Australian National University
  • Marcus Hutter Australian National University

Abstract

Most real-world problems have huge state and/or action spaces. Therefore, a naive application of existing tabular solution methods is not tractable on such problems. Nonetheless, these solution methods are quite useful if an agent has access to a relatively small state-action space homomorphism of the true environment and near-optimal performance is guaranteed by the map. A plethora of research is focused on the case when the homomorphism is a Markovian representation of the underlying process. However, we show that nearoptimal performance is sometimes guaranteed even if the homomorphism is non-Markovian.

Published
2019-07-17
Section
AAAI Technical Track: Planning, Routing, and Scheduling