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cs.LG2026
Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning
Yuan Zhuang, Yuexin Bian, Sihong He +7
Scaling critic capacity is a promising direction for improving off-policy reinforcement learning (RL). However, recent work shows that larger critics are prone to overfitting and i…
cs.LG2026
RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning
Yuexin Bian, Jie Feng, Tao Wang +3
On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies,…
cs.LG2025
Analytical Lyapunov Function Discovery: An RL-based Generative Approach
Haohan Zou, Jie Feng, Hao Zhao +1
Despite advances in learning-based methods, finding valid Lyapunov functions for nonlinear dynamical systems remains challenging. Current neural network approaches face two main is…