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

Faizan Ahmed, Aniket Dixit, James Brusey

TL;DR

The paper tackles the problem of erratic, energy-inefficient RL control in real-world systems by introducing higher-order action regularization, focusing on jerk minimization. The authors augment the MDP with action history and formulate first-, second-, and third-order penalties, comparing their impact across four continuous-control benchmarks and a building HVAC validation. They find that third-order penalties produce the smoothest policies with competitive rewards, and in HVAC reduce equipment switching by about 60%, translating to energy and longevity benefits. The work demonstrates that incorporating higher-order regularization can bridge RL optimization with practical operational constraints in energy-critical applications.

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.

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

TL;DR

The paper tackles the problem of erratic, energy-inefficient RL control in real-world systems by introducing higher-order action regularization, focusing on jerk minimization. The authors augment the MDP with action history and formulate first-, second-, and third-order penalties, comparing their impact across four continuous-control benchmarks and a building HVAC validation. They find that third-order penalties produce the smoothest policies with competitive rewards, and in HVAC reduce equipment switching by about 60%, translating to energy and longevity benefits. The work demonstrates that incorporating higher-order regularization can bridge RL optimization with practical operational constraints in energy-critical applications.

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.
Paper Structure (15 sections, 2 equations, 1 figure, 1 table)

This paper contains 15 sections, 2 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem formulation and methodology
  4. Markov decision process with action history
  5. Higher-order derivative penalties
  6. Experimental setup
  7. Results
  8. Evaluation metrics
  9. Continuous control benchmarks
  10. Building energy management validation
  11. Analysis and discussion
  12. Why higher-order penalties excel
  13. Energy efficiency mechanisms
  14. Limitations and future work
  15. Conclusion

Figures (1)

  • Figure 1: HVAC control performance vs smoothness trade-off. Third-order regularization achieves optimal position in the upper-right quadrant, indicating both high performance and superior smoothness.