Geometrically Averaged Hard Target Updates for Linear Q-Learning

The paper introduces the $\lambda$-target update mechanism for linear Q-learning, which employs geometrically averaged hard target updates to enhance stability in Q-learning with function approximation. The method allows for a continuum of target update strategies, ranging from one-period updates to projected Q-value iteration, and is analyzed using a switching-system model. This advancement is significant for practitioners as it provides a more flexible and potentially more stable approach to target updates in reinforcement learning, particularly in environments where linear function approximation is utilized.