Mean-Expansion Layer Accelerates Q-Learning by Sharing Values Across Actions

A recent study published on arXiv (ID: 2606.29806) presents the mean-expansion layer, a novel approach aimed at enhancing action-value learning within reinforcement learning (RL). This work tackles the limitations of conventional Q-learning methods, which update each state-action pair independently and often need numerous iterations to adjust values from their initial state (usually near zero) to their actual values, which can be significantly different. By sharing values across actions within a state, the mean-expansion layer shifts the focus from learning potentially large action-values to acquiring a lower-norm representation. In deep RL, this layer can be incorporated as a parameter-free addition to Q-network designs without changing the fundamental algorithm. The abstract of the paper has been revised as a replace-cross announcement, underscoring its significance in advancing efficiency in reinforcement learning within artificial intelligence and machine learning.