Q Ensemble

Q-ensembles in reinforcement learning leverage multiple Q-function networks to improve the robustness and accuracy of value estimations, mitigating the overestimation bias that plagues single Q-learning agents. Current research focuses on enhancing ensemble diversity through techniques like self-attention mechanisms, spiked random matrix regularization, and careful batch size scaling to optimize training efficiency. These advancements are significantly improving performance in both online and offline reinforcement learning settings, particularly for complex tasks and limited datasets, leading to more reliable and efficient agent training. The resulting improvements have implications for various applications, including robotics and control systems.