Paper recorded by Signals 4 on 2026-09-21 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-21 on arXiv · recorded by Signals 4 on 2026-09-22
Category: cs.LG · 机器学习 · first seen 2026-09-22
The growing demand for real-time, data-driven decision-making in complex and dynamic systems is placing increasing pressure on traditional Operational Research (OR) methodologies. Reinforcement learning (RL) has emerged as a complementary approach, offering strong learning and computational capabilities for sequential decision-making in dynamic and uncertain environments. Recent research shows an