Implementation and comparison of solution methods for decision processes with non-markovian rewards

  • Authors:

  • Charles Gretton;David Price;Sylvie Thiébaux

  • Affiliations:

  • Computer Sciences Laboratory, The Australian National University, Canberra, ACT, Australia;Computer Sciences Laboratory, The Australian National University, Canberra, ACT, Australia;Computer Sciences Laboratory, The Australian National University, Canberra, ACT, Australia

  • Venue:
  • UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
  • Year:
  • 2002

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Abstract

This paper examines a number of solution methods for decision processes with non-Markovian rewards(NMRDPs). They all exploit a temporal logic specification of the reward function to automatically translate the NMRDP into an equivalent Markov decision process (MDP) amenable to well-known MDP solution methods. They differ however in the representation of the target MDP and the class of MDP solution methods to which they are suited. As a result, they adopt different temporal logics and different translations. Unfortunately, no implementation of these methods nor experimental let alone comparative results have ever been reported. This paper is the first step towards filling this gap. We describe an integrated system for solving NMRDPs which implements these methods and several variants under a common interface; we use it to compare the various approaches and identify certain problem features favouring one over the other.