A reinforcement learning approach for dynamic multi-objective optimization
From MaRDI portal
Publication:2055564
DOI10.1016/j.ins.2020.08.101zbMath1475.90099OpenAlexW3084357507MaRDI QIDQ2055564
Fei Zou, Lixin Tang, Gary G. Yen, Chun-Feng Wang
Publication date: 1 December 2021
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2020.08.101
Multi-objective and goal programming (90C29) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (4)
Recruitment-imitation mechanism for evolutionary reinforcement learning ⋮ Digital twin-enabled dynamic scheduling with preventive maintenance using a double-layer Q-learning algorithm ⋮ Temporal distribution-based prediction strategy for dynamic multi-objective optimization assisted by GRU neural network ⋮ A fast algorithm to solve large-scale matrix games based on dimensionality reduction and its application in multiple unmanned combat air vehicles attack-defense decision-making
Uses Software
Cites Work
- Unnamed Item
- Evolutionary multi-objective optimization in uncertain environments. Issues and algorithms
- Convergence results for single-step on-policy reinforcement-learning algorithms
- A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization
- Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm
- Practical issues in temporal difference learning
- \({\mathcal Q}\)-learning
- Global memory schemes for dynamic optimization
- A modified version of a T-Cell Algorithm for constrained optimization problems
- Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization
This page was built for publication: A reinforcement learning approach for dynamic multi-objective optimization
