This short note presents a method of combining multiple policies in a given policy set such that the resulting policy improves all policies in the set for risk-sensitive exponential average reward Markov decision processes (MDPs), extending the work of Howard and Matheson for the singleton policy set case. Some applications of the method in solving risk-sensitive MDPs are also discussed.