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 decision-theoretic 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 decision-theoretic planning with non-Markovian rewards. The current
version of NMRDPP implements, under a single interface, a family of methods
based on existing as well as new approaches which we describe in detail. These
include dynamic programming, heuristic search, and structured methods. Using
NMRDPP, we compare the methods and identify certain problem features that
affect their performance. NMRDPPs treatment of non-Markovian rewards is
inspired by the treatment of domain-specific search control knowledge in the
TLPlan planner, which it incorporates as a special case. In the First
International Probabilistic Planning Competition, NMRDPP was able to compete
and perform well in both the domain-independent and hand-coded tracks, using
search control knowledge in the latter.
