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Liangfa Chen

2 accepted papers

2025

LipsNet++: Unifying Filter and Controller into a Policy Network

ICML 2025spotlight

Deep reinforcement learning (RL) is effective for decision-making and control tasks like autonomous driving and embodied AI. However, RL policies often suffer from the action fluctuation problem in real-world applications, resulting in severe actuator wear, safety risk, and performance degradation.…

2025

ODE-based Smoothing Neural Network for Reinforcement Learning Tasks

ICLR 2025spotlight

The smoothness of control actions is a significant challenge faced by deep reinforcement learning (RL) techniques in solving optimal control problems. Existing RL-trained policies tend to produce non-smooth actions due to high-frequency input noise and unconstrained Lipschitz constants in neural net…

Cited by 0SourcePDFScholar