Hierarchical Reinforcement Learning with Runtime Safety Shielding for Power Grid

arXiv:2508.20784v2 Announce Type: replace Abstract: Bus bunching remains a challenge for urban transit due to stochastic traffic and passenger demand. Traditional solutions rely

arXiv:2608.00679v1 Announce Type: new Abstract: Safety interventions for large populations of network-coupled agents must protect shared constraints without unnecessarily

arXiv:2606.24010v1 Announce Type: new Abstract: Multi-agent systems are widely used in safety-critical applications that require coordinated behavior under strict safety

arXiv:2609.36052v1 Announce Type: new Abstract: Power system operation is a safety-critical sequential decision-making problem, making it a natural testbed for reinforcement

arXiv:2605.04373v1 Announce Type: cross Abstract: RL-based controllers achieve strong average-case performance in networking tasks such as congestion control and adaptive bitrate

arXiv:2604.18789v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical

arXiv:2603.25747v1 Announce Type: new Abstract: The rapid evolution of Large Multimodal Models (LMMs) has enabled agents to perform complex digital and physical tasks, yet their

arXiv:2605.29078v1 Announce Type: new Abstract: Event-driven scheduling policies are increasingly deployed in industrial environments, where decisions are made under asynchronous