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Estimating Objective Weights of Pareto-Optimal Policies for Multi-Objective Sequential Decision-Making.
- Source :
-
Journal of Advanced Computational Intelligence & Intelligent Informatics . Mar2024, Vol. 28 Issue 2, p393-402. 10p. - Publication Year :
- 2024
-
Abstract
- Sequential decision-making under multiple objective functions includes the problem of exhaustively searching for a Pareto-optimal policy and the problem of selecting a policy from the resulting set of Pareto-optimal policies based on the decision maker's preferences. This paper focuses on the latter problem. In order to select a policy that reflects the decision maker's preferences, it is necessary to order these policies, which is problematic because the decision-maker's preferences are generally tacit knowledge. Furthermore, it is difficult to order them quantitatively. For this reason, conventional methods have mainly been used to elicit preferences through dialogue with decision-makers and through one-to-one comparisons. In contrast, this paper proposes a method based on inverse reinforcement learning to estimate the weight of each objective from the decision-making sequence. The estimated weights can be used to quantitatively evaluate the Pareto-optimal policies from the viewpoints of the decision-makers preferences. We applied the proposed method to the multi-objective reinforcement learning benchmark problem and verified its effectiveness as an elicitation method of weights for each objective function. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13430130
- Volume :
- 28
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Journal of Advanced Computational Intelligence & Intelligent Informatics
- Publication Type :
- Academic Journal
- Accession number :
- 176129595
- Full Text :
- https://doi.org/10.20965/jaciii.2024.p0393