Deep Q Network

Deep Q-Networks (DQNs) are a type of deep reinforcement learning algorithm used to train agents to make optimal decisions in complex environments by learning to approximate the optimal action-value function (Q-function). Current research focuses on improving DQN efficiency and applicability across diverse domains, including robotics, traffic control, and resource management, often employing variations like Double DQN, Dueling DQN, and integrating DQNs with other techniques such as Model Predictive Control or Gaussian Mixture Models for improved performance. This work is significant because DQNs offer a powerful, model-free approach to solving challenging sequential decision-making problems, leading to advancements in autonomous systems and optimization across various scientific and engineering fields.