An Actor-Critic Deep Reinforcement Learning Framework for Multi-objective Sequential Decision-making

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Rezaei Gazik Mohammad Amir; Roayaei Mehdi

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Journal Paper

Paper Information

Journal: TABRIZ JOURNAL OF ELECTRICAL ENGINEERING Year:2025 | Volume:55 | Issue:2 Page(s): 291-299

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Information Journal Paper

Title

An Actor-Critic Deep Reinforcement Learning Framework for Multi-objective Sequential Decision-making

Author(s)

Rezaei Gazik Mohammad Amir | Roayaei Mehdi | Issue Writer Certificate

Keywords

Deep reinforcement learning‎

‎Recommender system‎

‎Actor-Critic‎

‎Multi-objective decision making

Abstract

Sequential decision making describes a situation where the decision maker makes successive observations of a process before a final decision is made. In real-world scenarios, multi-objective sequential decision-making problems have been common and pose multiple challenges for researchers in decision-making. Most studies in this area have traditionally focused on single-objective situations or converted multi-objective problems into single-objective ones by combining objectives into a single goal. In this article, a multi-objective deep reinforcement learning framework called "MACA," based on the actor-critic method is presented, to optimize and balance multiple conflicting objectives in dynamic environments over time. This framework learns different policies for various objectives and eventually converges them to a global optimal policy. This framework, is evaluated in the domain of recommender systems for two conflicting objectives: accuracy (the desirability of recommended items for users) and fairness (the selection of recommended items from all categories); and, compared with other recent multi-objective reinforcement learning methods. Experimental results on the benchmark problem (recommender systems) demonstrate that this framework outperforms previous works in terms of performance (the accuracy was 92.5% with a fairness score of 96.5% on the Kiva dataset, and 93.1% accuracy with a fairness score of 97.6% on the MovieLens dataset), convergence time, and memory consumption. Moreover, the proposed framework is scalable with respect to the number of objectives and enables optimization of the variable number of objectives.

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