3 papers
cs.LG2026
Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
Xu Wan, Chao Yang, Cheng Yang +2
Safe Reinforcement Learning (RL) is crucial for achieving high performance while ensuring safety in real-world applications. However, the complex interplay of multiple uncertainty…
cs.AI2025
SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning
Xu Wan, Chao Yang, Cheng Yang +2
Although multi-agent reinforcement learning (MARL) has shown its success across diverse domains, extending its application to large-scale real-world systems still faces significant…
cs.AI2024
GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent
Hongtai Zeng, Chao Yang, Yanzhen Zhou +2
Ensuring that the outputs of neural networks satisfy specific constraints is crucial for applying neural networks to real-life decision-making problems. In this paper, we consider…