Abstract:
Reinforcement learning which is applied to multiobjective optimization problem is called multi-objective reinforcement learning. Related works in the study field of the m...Show MoreMetadata
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.
Published in: 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA)
Date of Conference: 11-12 November 2017
Date Added to IEEE Xplore: 14 December 2017
ISBN Information: