arXiv:2606. 25357v1 Announce Type: new Abstract: State abstraction plays a key role in scaling reinforcement learning to complex but structured systems.
By Yivan Zhang, Ziyan Luo, Manuel Baltieri
arXiv:2607. 16210v1 Announce Type: new Abstract: Reinforcement learning (RL) is increasingly applied in complex, safety-critical domains, yet the lack of rigorous behavioral guarantees for neural network-based policies remains a major barrier to deployment.
By Luca Marzari, Ezio Bartocci, Enrico Marchesini
arXiv:2510. 03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization.
By Volodymyr Tkachuk, Csaba Szepesv\'ari, Xiaoqi Tan
arXiv:2608. 19836v1 Announce Type: cross Abstract: Probabilistic shielding is a technique for safe reinforcement learning (RL).
By Astrid Horn Brorholt (Aalborg University, Aalborg, Denmark), Maris F. L. Galesloot (Radboud University, Nijmegen, Netherlands), Nils Jansen (Radboud University, Nijmegen, Netherlands), Kim Guldstrand Larsen (Aalborg University, Aalborg, Denmark), Christian Schilling (Aalborg University, Aalborg, Denmark)
arXiv:2511.02605v3 Announce Type: replace
Abstract: Shielding is widely used to enforce safety in reinforcement learning (RL), ensuring that an agent's actions remain compliant with formal specificat...
By Tiberiu-Andrei Georgescu, Alexander W. Goodall, Dalal Alrajeh, Francesco Belardinelli, Sebastian Uchitel
arXiv:2606. 01868v1 Announce Type: new Abstract: Reinforcement Learning (RL) has long served as a model for goal-directed animal behavior in neuroscience.
By Manu Srinath Halvagal, Sebastian Lee, SueYeon Chung