Towards Reliable Deep Reinforcement Learning for Industrial Applications: A DDPG-based Algorithm with Improved Performance

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Dolati Mahdi; Sayyaf Negin

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Journal Paper

Paper Information

Journal: AUT JOURNAL OF MECHANICAL ENGINEERING Year:2026 | Volume:10 | Issue:1 Page(s): 61-74

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Information Journal Paper

Title

Towards Reliable Deep Reinforcement Learning for Industrial Applications: A DDPG-based Algorithm with Improved Performance

Author(s)

Dolati Mahdi | Sayyaf Negin | Issue Writer Certificate

Keywords

deep reinforcement learning

Model-Based Method

Deep Deterministic Policy Gradient

Industrial Applications

System identification

Abstract

This paper proposes Improved Model-Based Deep Deterministic Policy Gradient (IMB-DDPG), a novel reinforcement learning algorithm designed to overcome three critical challenges in industrial DRL applications: (1) poor sample efficiency requiring excessive real-world trials, (2) safety risks from unstable policies during training, and (3) difficulty scaling to high-dimensional continuous control spaces. Building on DDPG's strengths for continuous control, IMB-DDPG introduces four key innovations: (i) a Virtual Environment (VE) for data-efficient learning, (ii) a Simulation Rate (SR) mechanism adapting model reliance dynamically, (iii) a Simulated Experience Buffer (SEB) preventing divergence, and (iv) a Performance Threshold (PT) for fail-safe operation. Evaluated on Cart-Pole benchmark via OpenAI Gym python library, IMB-DDPG demonstrates faster convergence than standard DDPG while maintaining performance degradation under sensor malfunctions or communication losses. These improvements derive from the algorithm's unique ability to simultaneously leverage real-world data and model-generated experiences, reducing physical trial costs while ensuring operational safety. The results establish IMB-DDPG as a practical solution for industrial control systems where reliability and data efficiency are paramount, particularly in applications like chemical process control and precision robotics that demand stable operation amid sensor/communication failures.

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