<ul><li>Efficient maintenance is crucial for engineering systems, especially with the challenges of Industry 4.0.</li><li>Reinforcement learning is increasingly used for maintenance optimization in engineering.</li><li>A novel maintenance model is introduced in this paper where repairs become increasingly imperfect over time.</li><li>A reinforcement-learning-based agent using Double Deep Q-Network architecture is developed to create maintenance policies, showing flexibility and cost improvements.</li></ul>

A reinforcement learning agent for maintenance of deteriorating systems with increasingly imperfect repairs

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