A reinforcement learning algorithm to improve scheduling search heuristics with the SVM

A regret-based biased random sampling scheme (RBRS) is a simple but powerful priority-rule based method to solve the resource constrained project scheduling problem (RCPSP), a well-known NP-hard benchmark problem. We present a generic machine learning method to improve results of RBRS. The rout-algorithm of reinforcement learning is combined with the support vector machine (SVM) to learn an appropriate value function which guides the search strategy given by RBRS. The specific properties of the SVM allow to reduce the size of the training set and show improved results even after a short period of training as demonstrated for benchmark instances of the RCPSP.