Higher-Order Action Regularization in Deep Reinforcement Learning: From Continuous Control to Building Energy Management

Faizan Ahmed; Aniket Dixit; James Brusey

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arXiv · 2601.02061

Higher-Order Action Regularization in Deep Reinforcement Learning: From Continuous Control to Building Energy Management

Faizan Ahmed·Aniket Dixit·James Brusey

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Abstract

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.

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Faizan Ahmed, Aniket Dixit, James Brusey. 2026-01-05. Higher-Order Action Regularization in Deep Reinforcement Learning: From Continuous Control to Building Energy Management. https://arxiv.org/abs/2601.02061

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