arXiv Machine Learning
Sep 24

Limiting-Kernel Q($\lambda$): Bridging Short and Long Horizons

Limiting‑Kernel Q(λ) (LKQL) is an off‑policy value estimator that blends n‑step truncation with a long‑horizon approximation based on the limiting kernel. It maintains the computational efficiency of n‑step methods while improving policy evaluation accuracy, especially for long‑horizon tasks. The authors prove faster convergence of LKQL’s operator under aperiodicity and near‑on‑policy conditions, and demonstrate empirical gains on MuJoCo continuous‑control benchmarks.

By Tolga Ok, Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Mohamad Amin Sharifi Kolarijani
arXiv AI
Oct 16

V-SHARE: Provable Convergence Guarantees for Advantage-based Algorithms

V‑SHARE is an advantage‑based value‑learning algorithm that blends cross‑action value sharing with an importance‑weighted Bellman‑residual correction. The paper presents the first full theoretical analysis of such an algorithm, offering both asymptotic convergence and finite‑sample guarantees. It also introduces schedules that balance deterministic contraction against importance‑weighting noise, and a two‑timescale analysis that eliminates a residual‑weight threshold while maintaining convergence.

By Kintan Saha, Hamed Masoudi, Xinyi Sheng, Dominik Baumann, Joni Pajarinen, Arsenii Mustafin