mlsanityData to 14 Oct 2026

Augmenting PID Control with Deep Reinforcement Learning: A Hybrid Approach to the Industrial Benchmark

arXiv 2609.22584 ↗First posted 18 Sep 2026cs.LGcited by 0

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

Abstract (arXiv)

As industrial processes grow in complexity, traditional Proportional-Integral-Derivative (PID) controllers are often insufficient for handling their non-linear, multi-input dynamics. We propose using advanced Deep Reinforcement Learning (DRL) to prove its advantages in these complex environments. To do this, we rely on the Industrial Benchmark (IB). The IB is a realistic simulation that tests DRL algorithms against the key challenges of industrial applications: high-dimensional state spaces, delayed effects, and conflicting multi-criterial objectives. This testbed highlights DRL's core trade-off: while its final policies can often be unstable, its unique strength is the ability to autonomously discover optimal, non-obvious policies in multi-dimensional spaces where simple controllers fail. In this paper, we propose a novel hybrid PID-RL controller that leverages DRL's discovery capability while ensuring Reliability. After developing a multi-objective reward function to make DRL viable, we use a twin-delayed deep deterministic (TD3) agent as a discovery tool to find the optimal, non-obvious settings for the IB's 'Gain' and 'Shift' parameters. By feeding these discovered parameters to a simple, tuned PID controller, our hybrid model successfully combines all three characteristics: it achieves the optimal Performance and Efficiency of the best DRL agent with the Reliability of a classical controller. This work demonstrates a practical methodology for using DRL to augment, rather than replace, trusted industrial control systems.

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 10 of its 33 references that are in the map. First posted on arXiv 18 Sep 2026.

Builds on

10 of 33 references.

References · 10

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  5. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  6. 2020What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Studycited by 162
  7. 2021d3rlpy: An Offline Deep Reinforcement Learning Librarycited by 84
  8. 2017A Benchmark Environment Motivated by Industrial Control Problemscited by 31
  9. 2021Return-based Scaling: Yet Another Normalisation Trick for Deep RLcited by 23
  10. 2023Deep reinforcement learning for process design: Review and perspectivecited by 10

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  3. 2025Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Control
  4. 2024Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems
  5. 2025Trustworthy and Explainable Deep Reinforcement Learning for Safe and Energy-Efficient Process Control: A Use Case in Industrial Compressed Air Systems
  6. 2026The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems
  7. 2025Intersection of Reinforcement Learning and Bayesian Optimization for Intelligent Control of Industrial Processes: A Safe MPC-based DPG using Multi-Objective BO
  8. 2025Nuclear Microreactor Control with Deep Reinforcement Learning