A Reinforcement Learning MVP (Minimum Viable Product) for Condition-Based Maintenance (CBM) using industrial equipment temperature sensor data. This project implements a sophisticated QR-DQN (Quantile Regression Deep Q-Network) agent to learn optimal maintenance policies balancing risk mitigation and cost minimization.
reinforcement-learning temperature-sensor sensor-data deep-q-learning quantile-regression cost-optimization predictive-maintenance risk-mitigation qr-dqn distributional-rl condition-based-maintenance prognostics-health-management disaster-resilience equipment-maintenance infrastructure-prognostics risk-sensitive-rl
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Updated
Dec 21, 2025 - Python