WSEAS Transactions on Information Science and Applications
Print ISSN: 1790-0832, E-ISSN: 2224-3402
Volume 22, 2025
Industrial Applications of Q-Learning: A Systematic Review
Authors: , , , ,
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Abstract: Artificial Intelligence (AI) couples various computational methods to replicate human learning and is crucial for addressing intricate problems. A significant aspect of AI is reinforcement learning (RL), which comprises algorithms that can resolve practical challenges, with Q-learning being a prominent example. Q-learning enhances the learning process, serving as a fundamental element of reinforcement learning, to discover optimal solutions without relying on a specific policy. As the demand for AI applications increases, Q-learning has gained traction in numerous sectors such as energy management, robotics, finance, game design, medicine, and logistics. This paper provides a concise overview of Q-learning applications across different industries, examining their main results, challenges, and limitations.
Keywords:
Q-learning, off-policy, machine learning, reinforcement learning, artificial intelligence, systematic review
Pages: 525-539
DOI: 10.37394/23209.2025.22.43