5 citations · 5 across the 16 of their papers we have counts for
10 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…
Distributional Soft Bellman Operator under the Cramér Geometry
Keru Wang, Yixin Deng, Yao Lyu +2
Distributional soft policy iteration (DSPI) provides an important framework for combining distributional reinforcement learning (DRL) with maximum-entropy control, in which the pol…
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…
A Spectral Revisit of the Distributional Bellman Operator under the Cramér Metric
Keru Wang, Yixin Deng, Yao Lyu +2
Distributional reinforcement learning (DRL) studies the evolution of full return distributions under Bellman updates rather than focusing on expected values. A classical result is…
Real-Time Generative Policy via Langevin-Guided Flow Matching for Autonomous Driving
Tianze Zhu, Yinuo Wang, Wenjun Zou +6
Reinforcement learning (RL) is a fundamental methodology in autonomous driving systems, where generative policies exhibit considerable potential by leveraging their ability to mode…
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…