“Stochastic Constraint Programming As Reinforcement Learning”, 2017-04-24 (; backlinks; similar):
Stochastic Constraint Programming (SCP) is an extension of Constraint Programming (CP) used for modeling and solving problems involving constraints and uncertainty. SCP inherits excellent modeling abilities and filtering algorithms from CP, but so far it has not been applied to large problems.
Reinforcement Learning (RL) extends Dynamic Programming to large stochastic problems, but is problem-specific and has no generic solvers.
We propose a hybrid combining the scalability of RL with the modeling and constraint filtering methods of CP.
We implement a prototype in a CP system and demonstrate its usefulness on SCP problems.