Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand

Keyphrases

• Adaptation 100%
• Variable Demand 100%
• Demand Distribution 100%
• Multi-agent Reinforcement Learning 100%
• Autonomous Routing 100%
• Future Demand 66%
• Demand Model 66%
• Operations Research 33%
• Urban Environment 33%
• Autonomous Vehicles 33%
• High Variability 33%
• Reinforcement Learning 33%
• San Francisco 33%
• Learning Framework 33%
• Taxicabs 33%
• Adaptivity 33%
• Adaptive Change 33%
• Demand Conditions 33%
• Wasserstein Distance 33%
• Numerical Results 33%
• Fluctuating Demand 33%
• Learning Scheme 33%
• Approximation Scheme 33%
• Historical Demand 33%
• Actual Demand 33%
• Online Play 33%
• Validity Region 33%
• Wasserstein Ambiguity 33%
• City Map 33%
• Wait Times 33%
• Non-myopic 33%

Computer Science

• Reinforcement Learning 100%
• multi-agent 100%
• System Analysis 50%
• Urban Environment 50%
• Autonomous Vehicles 50%
• Learning Framework 50%
• Approximation (Algorithm) 50%
• Wait Time 50%
• Learning Scheme 50%
• approximation scheme 50%
• Operations Research 50%