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cs.LG2026
Revisiting Temporal Regularization for Smooth Control in Deep Reinforcement Learning
SungJae Ahn, Jeong Woon Lee, Kyoleen Kwak +1
Deep Reinforcement Learning policies can produce nonsmooth action oscillations that hinder deployment on physical robots. Existing architectural and penalty-based approaches seek s…
cs.LG2026
Enhancing Control Policy Smoothness by Aligning Actions with Predictions from Preceding States
Kyoleen Kwak, Hyoseok Hwang
Deep reinforcement learning has proven to be a powerful approach to solving control tasks, but its characteristic high-frequency oscillations make it difficult to apply in real-wor…
cs.LG2026
Stabilizing the Q-Gradient Field for Policy Smoothness in Actor-Critic Methods
Jeong Woon Lee, Kyoleen Kwak, Daeho Kim +1
Policies learned via continuous actor-critic methods often exhibit erratic, high-frequency oscillations, making them unsuitable for physical deployment. Current approaches attempt…