This paper introduces a novel multi-state reinforcement learning-based multi-objective evolutionary algorithm, MRL-MOEA, which utilizes reinforcement learning ...

Aug 28, 2024 · This paper introduces a novel multi-state reinforcement learning-based multi-objective evolutionary algorithm, MRL-MOEA, which utilizes reinforcement learning ...

Request PDF | On Aug 1, 2024, Jing Wang and others published A Novel Multi-State Reinforcement Learning-Based Multi-Objective Evolutionary Algorithm | Find, ...

F. C. Fernandez, W. Caarls, Parameters tuning and optimization for reinforcement learning algorithms using evolutionary computing, in: 2018 International ...

The Proximal Distilled Evolutionary Reinforcement Learning (PDERL) framework succeeds in combining the robustness of EA and the efficiency of RL methods.

Recently, the authors utilized genetic algorithms to develop a multi-objective optimization framework that aims to optimize spectral efficiency and transmission ...

This paper introduces multi-objective reinforcement learning based on decomposition (MORL/D), a novel methodology bridging the literature of RL and MOO.

This article proposes a novel multi-agent deep reinforcement learning (MADRL)-based optimization algorithm.

Indicator-based evolutionary algorithms are amongst the best performing methods for solving multi-objective optimization (MOO) problems. In reinforcement ...

Feb 11, 2022 · This paper proposes a novel operator selection method based on reinforcement learning. In the proposed method, the decision variables are regarded as states.