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cs.LG2024
Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents
Chung-En Sun, Sicun Gao, Tsui-Wei Weng
Robustness remains a paramount concern in deep reinforcement learning (DRL), with randomized smoothing emerging as a key technique for enhancing this attribute. However, a notable…
cs.LG2024★ 1 cited
Activation-Descent Regularization for Input Optimization of ReLU Networks
Hongzhan Yu, Sicun Gao
We present a new approach for input optimization of ReLU networks that explicitly takes into account the effect of changes in activation patterns. We analyze local optimization ste…
cs.RO2024
Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller
Mingxin Yu, Chenning Yu, M-Mahdi Naddaf-Sh +3
Sampling-based motion planning methods for manipulators in crowded environments often suffer from expensive collision checking and high sampling complexity, which make them difficu…