Multi-objective reinforcement learning algorithm for MOSDMP in unknown environment

In this paper, a new multi-objective reinforcement learning algorithm for multi-objective sequential decision making problems in unknown environment is proposed. The salient characters of the algorithm are: (1) decision maker´s objective preference is introduced to guide learning direction; (2) a new measure of comparing action decisions under several objectives based on the fuzzy inference system is defined; (3) fast learning speed can be achieved. Simulation results demonstrate that the proposed algorithm has a good learning performance.