Elevator Systems and Control

Neueste Paper

• Fully Offline Reinforcement Learning

  Mattie Fellows, Clarisse Wibault, Uljad Berdica, Johannes Forkel, Maike Osborne, Jakob N. Foerster · 17. Juli 2026

  Offline RL (ORL) promises safe and sample-efficient deployment but existing methods rely on undocumented online interactions for hyperparameter tuning and lack reliable fully offline estimates of initial online performance. We introduce SOReL, a fully offline Bayesian model-based RL method that lear…

• PIQL: Projective Implicit Q-Learning with Support Constraint for Offline Reinforcement Learning

  Xinchen Han, Hossam Afifi, Michel Marot · 3. Februar 2026

  Offline Reinforcement Learning (RL) faces a fundamental challenge of extrapolation errors caused by out-of-distribution (OOD) actions. Implicit Q-Learning (IQL) employs expectile regression to achieve in-sample learning. Nevertheless, IQL relies on a fixed expectile hyperparameter and a density-base…

• Dynamic Exploration on Segment-Proposal Graphs for Tubular Centerline Tracking

  Chong Di, Jinglin Zhang, Zhenjiang Li, Jean-Marie Mirebeau, Da Chen, Laurent D. Cohen · 23. Januar 2026

  Optimal curve methods provide a fundamental framework for tubular centerline tracking. Point-wise approaches, such as minimal paths, are theoretically elegant but often suffer from shortcut and short-branch combination problems in complex scenarios. Nonlocal segment-wise methods address these issues…

• Asynchronous Stochastic Approximation with Applications to Average-Reward Reinforcement Learning

  Huizhen Yu, Yi Wan, Richard S. Sutton · 10. Dezember 2025

  This paper investigates the stability and convergence properties of asynchronous stochastic approximation (SA) algorithms, with a focus on extensions relevant to average-reward reinforcement learning. We first extend a stability proof method of Borkar and Meyn to accommodate more general noise condi…

• Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning

  Emile Anand, Ishani Karmarkar, Guannan Qu · 27. Oktober 2025

• On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning

  Anas Barakat, Souradip Chakraborty, Peihong Yu, Pratap Tokekar, Amrit Singh Bedi · 27. Oktober 2025