mlsanityData to 14 Oct 2026

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

arXiv 2601.02061 ↗First posted 5 Jan 2026cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 11 references that are in the map. First posted on arXiv 5 Jan 2026.

Builds on

4 of 11 references.

References · 4

  1. 2021Convex optimizationcited by 2,515
  2. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
  3. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 371
  4. 2018Residual Reinforcement Learning for Robot Controlcited by 274

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  1. 2026Revisiting Temporal Regularization for Smooth Control in Deep Reinforcement Learning
  2. 2024Benchmarking Smoothness and Reducing High-Frequency Oscillations in Continuous Control Policies
  3. 2025Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning
  4. 2026Enhancing Control Policy Smoothness by Aligning Actions with Predictions from Preceding States
  5. 2026Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility
  6. 2024Gradient-based Regularization for Action Smoothness in Robotic Control with Reinforcement Learning
  7. 2026Safe Deep Reinforcement Learning for Energy-Efficient HVAC Control in Multi-Zone Residential Buildings
  8. 2024An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control