Planning vs. Reinforcement Learning for Multi-Asset Maintenance

A new research paper compares planning and reinforcement learning (RL) approaches for multi-asset maintenance scheduling. The study found that planning methods enforce reliability as a strict constraint, leading to zero-failure policies that are largely unaffected by failure penalty magnitudes. In contrast, RL agents optimize for expected cost, sometimes accepting occasional failures for lower overall costs, especially when penalties are low. The research suggests planning is better for strict reliability and short horizons, while RL is more cost-efficient for scenarios where some failures are acceptable. AI

IMPACT Clarifies trade-offs between planning and RL for maintenance scheduling, suggesting planning for strict reliability and RL for cost efficiency with acceptable failures.

RANK_REASON Academic paper published on arXiv detailing a comparison of AI/planning methods for a specific industrial problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Action masking
• Failure penalty
• Multi-Asset Maintenance
• Planning approaches
• preventive maintenance
• reinforcement learning
• run-to-failure data

• paper
• other

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