Q Network

Q-networks are a core component of reinforcement learning (RL), used to estimate the value of taking specific actions in given states, guiding an agent towards optimal behavior. Current research focuses on improving Q-network efficiency and robustness, exploring techniques like adaptive hyperparameter tuning, multi-step Bellman updates, and incorporating uncertainty quantification into value function estimations. These advancements aim to enhance sample efficiency, generalization capabilities, and the applicability of RL to complex, high-dimensional problems across diverse domains, including robotics, game playing, and scientific modeling. The resulting improvements in RL algorithms have significant implications for various fields requiring automated decision-making under uncertainty.