Development of interactive multi-objective reinforcement learning considering preference structure of a decision maker

Abstract

Reinforcement learning which is applied to multiobjective optimization problem is called multi-objective reinforcement learning. Related works in the study field of the multiobjective Reinforcement Learning indicate that multi-objective reinforcement learning with a choice procedure based on Hypervolume is effective for finding Pareto optimal solution of multiobjective optimization problems. However, a selected Pareto optimal solution based on Hypervolume does not always match the preference of a decision maker. This study proposes interactive multi-objective reinforcement learning which can reflect the preference structure of a decision maker using scalarization method and interactive method after discovering Pareto optimal solution.