Deep reinforcement learning-based life-cycle management of deteriorating transportation systems

Keyphrases

• Optimization Problem 100%
• Learning-based 100%
• Transportation System 100%
• Life Cycle Management 100%
• Deep Reinforcement Learning (deep RL) 100%
• Management Policy 50%
• Risk Reduction 25%
• Pavement 25%
• Limited Resources 25%
• Multi-component System 25%
• Disjoint 25%
• Risk Factors 25%
• Condition-based Maintenance 25%
• Periodic Inspection 25%
• System Function 25%
• Transportation Network 25%
• Virginia 25%
• Monitoring Data 25%
• System Level 25%
• Uncertain Data 25%
• Sequential Decision 25%
• Complex Networks 25%
• Operational Reliability 25%
• Component Type 25%
• Cost Reduction 25%
• System Components 25%
• Management Agency 25%
• Deterministic Bounds 25%
• Mathematical Framework 25%
• Adept 25%
• Pavement Section 25%
• Transportation Infrastructure 25%
• Maintenance Policy 25%
• Health Data 25%
• Structural Health 25%
• Network Effects 25%
• Maintenance Techniques 25%
• Interconnected Systems 25%
• Traffic Risk 25%
• Inspection Policy 25%
• Broad-Complex 25%
• Probabilistic Constraints 25%
• Model Inference 25%
• Infrastructure Management 25%
• Data-limited 25%
• Imposed Constraint 25%
• Plan Optimization 25%
• Traffic Delay 25%
• Conventional Management 25%
• Deep Reinforcement Learning Algorithm 25%
• Multi-agent Actor-critic 25%
• Decentralized Multi-Agent 25%
• Constrained Partially Observable Markov Decision Processes 25%

Engineering

• Optimisation Problem 100%
• Lifecycle 100%
• Cycle Management 100%
• Reinforcement Learning 100%
• Optimality 50%
• Pavement 50%
• Structural Integrity 25%
• Policy Management 25%
• Complex Networks 25%
• Multicomponent System 25%
• Monitoring Data 25%
• System Component 25%
• Asset Management 25%
• Limited Resource 25%
• Structural Health 25%
• Paramount Importance 25%
• Deterioration Model 25%
• System Function 25%
• Partially Observable Markov Decision Process 25%
• Inspection Plan 25%
• Health Data 25%
• Decentralized Multi 25%