DoorDash Deploys Multi-Agent Reinforcement Learning to Optimize Food Delivery Dispatch
DoorDash researchers have developed and deployed a reinforcement learning system that automatically adjusts dispatch priorities in its food delivery marketplace using real operational feedback. The system learns from delayed signals like delivery speed and courier utilization to optimize the tradeoff between delivery quality and batching efficiency. This approach demonstrates how machine learning can safely improve logistics decisions in large-scale real-world systems while maintaining operational constraints.
Researchers at DoorDash have published details of a deployed reinforcement learning system designed to optimize dispatch decisions in three-sided food delivery marketplaces. Rather than replacing the existing combinatorial assignment optimizer, the system learns a store-level policy that selects discrete multipliers to adjust the optimizer's tradeoff between delivery quality and batching efficiency. The approach uses centralized offline learning from logged marketplace data combined with decentralized execution, employing Double Q-learning with conservative regularization to handle noisy, delayed, and coupled feedback signals. A production switchback experiment showed the offline-trained policy increased batching efficiency and reduced courier time costs without degrading customer-facing delivery quality. The work illustrates how reinforcement learning can be safely integrated into complex real-world economic and logistics systems while preserving production feasibility and operational safeguards.
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- arXiv cs.AICenter
Multi-Agent Reinforcement Learning from Delayed Marketplace Feedback for Objective-Weight Adaptation in Three-Sided Dispatch
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