“…In the multi-objective setting, the completion of a task requires the simultaneous satisfaction of multiple objectives such as balancing power consumption and performance in Web servers (Tesauro, et al, 2008). Such problems can be modeled as multi-objective Markov decision processes (MOMDPs) and solved by some existing multi-objective reinforcement learning (MORL) algorithms (Tesauro et al, 2008;Nguyen, 2018;Tajmajer, 2018;Abels, et al, 2018;Van Moffaert, Drugan, & Nowé, 2013;Vamplew, Dazeley, & Foale, 2017) based on the assumption that either the weighting factor for different objective functions or the ordering information is available. The solution for parameterized algorithms assumes the weighting factor for different objective functions can be obtained either directly (i.e., known a priori ) or indirectly (through learning).…”