Deep reinforcement learning agents often exhibit erratic, high-frequency control behaviors that hinder real-world deployment due to excessive energy consumption and mechanical wear. We systematically investigate action smoothness regularization through higher-order derivative penalties, progressing from theoretical understanding in continuous control benchmarks to practical validation in building energy management. Our comprehensive evaluation across four continuous control environments demonstrates that third-order derivative penalties (jerk minimization) consistently achieve superior smoothness while maintaining competitive performance. We extend these findings to HVAC control systems where smooth policies reduce equipment switching by 60%, translating to significant operational benefits. Our work establishes higher-order action regularization as an effective bridge between RL optimization and operational constraints in energy-critical applications.
@article{arxiv.2601.02061,
title = {Higher-Order Action Regularization in Deep Reinforcement Learning: From Continuous Control to Building Energy Management},
author = {Faizan Ahmed and Aniket Dixit and James Brusey},
journal= {arXiv preprint arXiv:2601.02061},
year = {2026}
}