Industrial Applications of Q-Learning: A Systematic Review

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.