Multi-Agent Reinforcement Learning from Delayed Marketplace Feedback for Objective-Weight Adaptation in Three-Sided Dispatch
arXiv:2606.13604v1 Announce Type: new Abstract: Dispatch in three-sided marketplaces provides a natural setting for reinforcement learning from world feedback: decisions are evaluated by delayed operational outcomes such as delivery speed, courier utilization, and merchant congestion. We present a deployed reinforcement learning system at DoorDash that adapts dispatch objective weights in a large-scale food-delivery marketplace using delayed signals. Rather than replacing the combinatorial assignment optimizer, a store-level policy learned from logged marketplace data selects a discrete multip
The increasing complexity of multi-sided marketplaces and the maturation of reinforcement learning techniques are converging to enable practical, large-scale agentic systems for operational optimization.
This development indicates a tangible application of AI agents in mission-critical logistics, demonstrating their ability to directly influence key business metrics and operational efficiency in real-world, high-volume environments.
Operational dispatch and resource allocation within complex ecosystems can now be dynamically optimized by AI agents learning from delayed, real-world feedback, moving beyond static rules or human-intensive adjustments.
- · Logistics companies
- · On-demand service platforms
- · AI software providers
- · Consumers (via improved service efficiency)
- · Companies reliant on static, rule-based optimization
- · Manual dispatch operators
Increased efficiency and profitability for platform businesses through automated objective-weight adaptation.
Broader adoption of multi-agent reinforcement learning in other complex operational domains beyond logistics.
The development of standardized frameworks and platforms for deploying and monitoring production-grade agentic systems based on real-world feedback.
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Read at arXiv cs.AI