Deep Q Learning

Deep Q-learning (DQL) is a reinforcement learning algorithm that trains agents to make optimal decisions by learning to approximate the optimal Q-function, which estimates the expected future reward for taking a specific action in a given state. Current research focuses on applying DQL to diverse problems, including autonomous navigation, resource management in networks (e.g., 5G and vehicular networks), and optimizing complex systems like traffic flow and agricultural practices, often incorporating deep neural networks and other advanced architectures like graph convolutional networks or transformers to handle high-dimensional data. The effectiveness and adaptability of DQL across these domains highlight its significance for solving complex decision-making problems in various fields, from robotics and AI safety to operations research and sustainable resource management.