Higher-Order Action Regularization in Deep Reinforcement Learning: From Continuous Control to Building Energy Management
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915708469772288 |
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| author | Ahmed, Faizan Dixit, Aniket Brusey, James |
| author_facet | Ahmed, Faizan Dixit, Aniket Brusey, James |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02061 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Higher-Order Action Regularization in Deep Reinforcement Learning: From Continuous Control to Building Energy Management Ahmed, Faizan Dixit, Aniket Brusey, James Artificial Intelligence Machine Learning 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. |
| title | Higher-Order Action Regularization in Deep Reinforcement Learning: From Continuous Control to Building Energy Management |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2601.02061 |