Intersection of Reinforcement Learning and Bayesian Optimization for Intelligent Control of Industrial Processes: A Safe MPC-based DPG using Multi-Objective BO

• URL: http://arxiv.org/abs/2507.09864v1
• Date: Mon, 14 Jul 2025 02:31:52 GMT
• Title: Intersection of Reinforcement Learning and Bayesian Optimization for Intelligent Control of Industrial Processes: A Safe MPC-based DPG using Multi-Objective BO
• Authors: Hossein Nejatbakhsh Esfahani, Javad Mohammadpour Velni,
• Abstract summary: Model Predictive Control (MPC)-based Reinforcement Learning (RL) offers a structured and interpretable alternative to Deep Neural Network (DNN)-based RL methods.<n>Standard MPC-RL approaches often suffer from slow convergence, suboptimal policy learning due to limited parameterization, and safety issues during online adaptation.<n>We propose a novel framework that integrates MPC-RL with Multi-Objective Bayesian Optimization (MOBO)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model Predictive Control (MPC)-based Reinforcement Learning (RL) offers a structured and interpretable alternative to Deep Neural Network (DNN)-based RL methods, with lower computational complexity and greater transparency. However, standard MPC-RL approaches often suffer from slow convergence, suboptimal policy learning due to limited parameterization, and safety issues during online adaptation. To address these challenges, we propose a novel framework that integrates MPC-RL with Multi-Objective Bayesian Optimization (MOBO). The proposed MPC-RL-MOBO utilizes noisy evaluations of the RL stage cost and its gradient, estimated via a Compatible Deterministic Policy Gradient (CDPG) approach, and incorporates them into a MOBO algorithm using the Expected Hypervolume Improvement (EHVI) acquisition function. This fusion enables efficient and safe tuning of the MPC parameters to achieve improved closed-loop performance, even under model imperfections. A numerical example demonstrates the effectiveness of the proposed approach in achieving sample-efficient, stable, and high-performance learning for control systems.

Related papers

• Computationally efficient Gauss-Newton reinforcement learning for model predictive control [0.8437187555622164]
  We introduce a Gauss-Newton approximation of the deterministic policy Hessian that eliminates the need for second-order policy derivatives.<n>We demonstrate the effectiveness of the approach on a nonlinear continuously stirred tank reactor.
  arXiv  Detail & Related papers  (2025-08-04T14:00:40Z)
• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• Sample-Efficient Reinforcement Learning of Koopman eNMPC [42.72938925647165]
  Reinforcement learning can be used to tune data-driven (economic) nonlinear model predictive controllers ((e)NMPCs) for optimal performance in a specific control task.<n>We combine a model-based RL algorithm with our published method that turns Koopman (e)NMPCs into automatically differentiable policies.
  arXiv  Detail & Related papers  (2025-03-24T15:35:16Z)
• A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning [61.403275660120606]
  Reinforcement learning (RL)-based fine-tuning has emerged as a powerful approach for aligning diffusion models with black-box objectives.<n>We propose leave-one-out PPO (LOOP), a novel RL for diffusion fine-tuning method.<n>Our results demonstrate that LOOP effectively improves diffusion models on various black-box objectives, and achieves a better balance between computational efficiency and performance.
  arXiv  Detail & Related papers  (2025-03-02T13:43:53Z)
• Predictive Lagrangian Optimization for Constrained Reinforcement Learning [15.082498910832529]
  Constrained optimization is popularly seen in reinforcement learning for addressing complex control tasks.<n>In this paper, we propose a more generic equivalence framework to build the connection between constrained optimization and feedback control system.
  arXiv  Detail & Related papers  (2025-01-25T13:39:45Z)
• Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter System [0.7499722271664147]
  This study conducts a comparative analysis of Model Predictive Control (MPC) and Proximal Policy Optimization (PPO), a Deep Reinforcement Learning (DRL) algorithm, applied to a Quanser Aero 2 system. PPO excels in rise-time and adaptability, making it a promising approach for applications requiring rapid response and adaptability.
  arXiv  Detail & Related papers  (2024-08-28T08:35:34Z)
• Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining [50.00291020618743]
  This work introduces a novel, parameter-adaptive AMPC architecture capable of online tuning without recomputing large datasets and retraining. We showcase the effectiveness of parameter-adaptive AMPC by controlling the swing-ups of two different real cartpole systems with a severely resource-constrained microcontroller (MCU) Taken together, these contributions represent a marked step toward the practical application of AMPC in real-world systems.
  arXiv  Detail & Related papers  (2024-04-08T20:02:19Z)
• Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning [1.4069478981641936]
  We propose a novel framework in which any parameter of the control algorithm can be jointly tuned using reinforcement learning(RL) We demonstrate our framework on the inverted pendulum control task, reducing the total time of the control system by 36% while also improving the control performance by 18.4% over the best-performing MPC baseline.
  arXiv  Detail & Related papers  (2021-11-07T18:33:22Z)
• Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization [76.51980153902774]
  Federated learning (FL) is vulnerable to external attacks on FL models during parameters transmissions. In this paper, we propose effective MP algorithms to combat state-of-the-art defensive aggregation mechanisms. Our experimental results demonstrate that the proposed CMP algorithms are effective and substantially outperform existing attack mechanisms.
  arXiv  Detail & Related papers  (2021-01-28T03:28:18Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)
• Information Theoretic Model Predictive Q-Learning [64.74041985237105]
  We present a novel theoretical connection between information theoretic MPC and entropy regularized RL. We develop a Q-learning algorithm that can leverage biased models.
  arXiv  Detail & Related papers  (2019-12-31T00:29:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.