Implementation and Comparison of Solution Methods for Decision Processes with Non-Markovian Rewards
This work addresses a gap in the field of decision processes for researchers, but it is incremental as it focuses on implementation and comparison rather than introducing new methods.
This paper tackles the lack of implementations and comparisons for solution methods in decision processes with non-Markovian rewards by developing an integrated system that implements multiple methods and uses it to compare approaches and identify favorable problem features.
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 the problem features favoring one over the other.