Geometrically Averaged Hard Target Updates for Linear Q-Learning
Researchers have introduced a new method called the $\lambda$-target update for linear Q-learning, which averages periodic target updates with geometric weights. This technique aims to improve the stability of Q-learning, particularly when using linear function approximation. The paper analyzes this mechanism using a switching-system model and notes its applicability to both deterministic and stochastic reinforcement learning scenarios. AI
IMPACT Introduces a novel technique for improving the stability of Q-learning algorithms, potentially benefiting reinforcement learning applications.