Researchers have developed a multi-agent reinforcement learning system for DoorDash that adapts dispatch objective weights using delayed marketplace feedback. The system, deployed at the store level, selects multipliers to adjust the trade-off between delivery quality and batching efficiency. This approach allows for offline policy learning with noisy and delayed signals, preserving operational safeguards. A production experiment showed the policy increased batching and reduced courier time costs without negatively impacting delivery quality. AI
IMPACT Demonstrates how reinforcement learning can optimize complex logistics systems with delayed feedback, potentially improving efficiency in other delivery platforms.
RANK_REASON The cluster contains an academic paper detailing a deployed system for a real-world application.
Read on arXiv cs.MA (Multiagent) →
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