Decision-Theoretic Planning with non-Markovian Rewards

A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decisiontheoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model. While the more tractable solution methods developed for MDPs do not directly apply in the presence of non-Markovian rewards, a number of solution methods for NMRDPs have been proposed in the literature. These all exploit a compact specification of the non-Markovian reward function in temporal logic, to automatically translate the NMRDP into an equivalent MDP which is solved using efficient MDP solution methods. This paper presents nmrdpp(Non-Markovian Reward Decision Process Planner), a software platform for the development and experimentation of methods for deci...

• Type: Journal
• Year: 2006
• Where: JAIR
• Authors: Sylvie Thiébaux, Charles Gretton, John K. Slaney, David Price, Froduald Kabanza
• Keywords: Decision Process, JAIR 2006, Non-Markovian Rewards, Solution Methods