4 papers · 1 filter
Momentum as Residual-Driven Multiplier Correction for Deep Learning Optimization
Zhixin Ren, Yao Lyu, Congrong Li +2
Momentum-based optimizers are widely used in modern deep learning, yet the relations among momentum recursion, update geometry, and acceleration remain only partially understood. W…
Augmented Lagrangian Multiplier Network for State-wise Safety in Reinforcement Learning
Jiaming Zhang, Yujie Yang, Yao Lyu +2
Safety is a primary challenge in real-world reinforcement learning (RL). Formulating safety requirements as state-wise constraints has become a prominent paradigm. Handling state-w…
Exchange Policy Optimization Algorithm for Semi-Infinite Safe Reinforcement Learning
Jiaming Zhang, Yujie Yang, Haoning Wang +2
Safe reinforcement learning (RL) aims to optimize long-term performance while adhering to safety requirements. However, many practical applications involve an infinite number of co…
Predictive Lagrangian Optimization for Constrained Reinforcement Learning
Tianqi Zhang, Puzhen Yuan, Guojian Zhan +6
Constrained optimization is popularly seen in reinforcement learning for addressing complex control tasks. From the perspective of dynamic system, iteratively solving a constrained…