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arxiv: 2606.13604 · v1 · pith:VTE45LMT · submitted 2026-06-11 · cs.AI · cs.LG· cs.MA

Multi-Agent Reinforcement Learning from Delayed Marketplace Feedback for Objective-Weight Adaptation in Three-Sided Dispatch

Haochen Wu , Yi Hou , Shiguang Xie This is my paper

Reviewed by Pith2026-06-27 06:45 UTCgrok-4.3pith:VTE45LMTopen to challenge →

classification cs.AI cs.LGcs.MA
keywords multi-agent reinforcement learningdelayed feedbackoffline RLdispatch optimizationmarketplace adaptationobjective weightsconservative regularization
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The pith

An offline-trained policy adapts dispatch weights in a live three-sided marketplace to increase batching while holding delivery quality steady.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows how reinforcement learning can tune the objective weights inside an existing combinatorial dispatch optimizer rather than replace it. A policy learned from historical marketplace logs chooses discrete multipliers that shift the balance between customer delivery times and courier batching efficiency. Training uses a centralized value function updated with Double Q-learning targets plus a conservative regularizer to limit overestimation on unseen states. The resulting policy is executed store-by-store and was tested in a production switchback experiment. If correct, the approach demonstrates that delayed, noisy operational feedback can safely drive online adaptation of high-stakes decisions without violating feasibility constraints.

Core claim

A store-level policy trained offline on logged marketplace data selects discrete multipliers for the dispatch optimizer's tradeoff between delivery quality and batching efficiency. Using centralized training of a shared value function with Double Q-learning targets and a conservative regularizer, the policy increases batching rates and reduces courier-side time costs in a production switchback experiment while leaving customer-facing delivery quality unchanged.

What carries the argument

A store-level policy that outputs discrete multipliers for the existing combinatorial assignment optimizer, trained via centralized offline value learning with Double Q-learning and a conservative regularizer to bound out-of-distribution overestimation.

Load-bearing premise

Logged marketplace data plus the conservative regularizer produces value estimates that stay reliable under live deployment without large distribution shift or violation of production constraints.

What would settle it

A new switchback period in which the policy either fails to increase batching, increases courier time costs, or degrades measured delivery quality relative to the baseline would falsify the claim that the learned policy improves the intended metrics.

Figures

Figures reproduced from arXiv: 2606.13604 by Haochen Wu, Shiguang Xie, Yi Hou.

Figure 1
Figure 1. Figure 1: Agentic objective-weight adaptation loop for production dispatch. During online serving, a policy agent observes local states and selects an objective-weight multiplier that parameterizes the assignment optimizer. The optimizer remains responsible for feasible courier-order assignment decisions. During offline learn￾ing, logged runs are joined with delayed marketplace outcomes to construct transition data … view at source ↗
Figure 2
Figure 2. Figure 2: Logistics timing compoents used for delayed reward attribution. ASAP measures customer-facing delivery duration, and XCAT measures excess courier active time (CAT) beyond the direct route. The RL-selected ASAP-weight multiplier steers the optimizer’s speed-efficiency tradeoff: higher weights favor faster delivery completion, while lower weights make batching-compatible assignments more attractive. Observed… view at source ↗
Figure 3
Figure 3. Figure 3: Empirical action distributions from DoorDash production logs during a Friday dinner peak, showing state-dependent ASAP￾weight multipliers across backlog, supply pressure, and CWT. San Francisco-Bay Area market during Friday dinner peak [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Offline training MSE loss across epochs. The DQN baseline without CQL regularization minimizes MSE faster, while OWA-RL maintains a higher loss due to the conservative penalty used to reduce unsupported-action overestimation. B. Policy Behavior Under Reward Reweighting Before deployment, we evaluate whether the learned policy responds directionally to reward design [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Predicted action distributions under different reward-weight settings. Changing the reward weights shifts the selected objective￾weight multipliers, showing that the learned policy adapts its behavior to the desired speed-efficiency tradeoff. 7 [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: shows the daily distribution of selected ASAP-weight multipliers, while [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Daily distribution of the supply-pressure indicator in production. Monitoring state-feature distributions helps detect marketplace drift that may affect policy reliability. 8 [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
read the original 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 multiplier that shifts the dispatch optimizer's tradeoff between delivery quality and batching efficiency. This interface enables offline policy learning under noisy, delayed, and coupled feedback while preserving production feasibility constraints and operational safeguards. We train a shared value function using centralized offline data and decentralized store-level execution, with Double Q-learning targets and a conservative regularizer to reduce out-of-distribution value overestimation. In a production switchback experiment, the offline-trained policy increases batching and reduces courier-side time costs without degrading customer-facing delivery quality. Results illustrate how world feedback from a live economic and logistics system can be used to safely adapt decision policies online.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript presents a deployed multi-agent RL system at DoorDash for adapting dispatch objective weights in a three-sided food-delivery marketplace. A store-level policy is trained offline on centralized logged data using Double Q-learning with a conservative regularizer; the policy selects discrete multipliers for an existing combinatorial dispatch optimizer. The approach preserves production feasibility constraints. Validation occurs via a production switchback experiment in which the learned policy increases batching, reduces courier-side time costs, and does not degrade customer-facing delivery quality.

Significance. If the central experimental result holds after addressing the noted gaps, the work supplies a concrete, production-validated template for offline RL under delayed, noisy, and coupled marketplace feedback. The centralized-training/decentralized-execution interface together with the conservative regularizer offers a practical route to objective-weight adaptation while respecting operational safeguards; this is a rare documented case of live economic-system feedback being used to adapt a deployed decision policy.

major comments (1)
  1. [Training and Experiment sections (abstract paragraph on training and experiment)] The production switchback experiment is presented as the primary validation that value estimates remain reliable under live deployment. However, the manuscript provides no explicit quantification of distribution shift (e.g., divergence in state-action occupancy between logged and policy-induced trajectories) nor additional off-policy evaluation on shifted subsets. Without such analysis, it is unclear whether the conservative regularizer alone suffices to bound overestimation when batching patterns, timing, or merchant/courier behavior change after deployment.
minor comments (1)
  1. [Abstract] The abstract states results qualitatively; reporting the magnitude of batching increase, courier time-cost reduction, and any statistical significance or confidence intervals from the switchback would strengthen the claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the distribution-shift analysis. We address the single major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Training and Experiment sections (abstract paragraph on training and experiment)] The production switchback experiment is presented as the primary validation that value estimates remain reliable under live deployment. However, the manuscript provides no explicit quantification of distribution shift (e.g., divergence in state-action occupancy between logged and policy-induced trajectories) nor additional off-policy evaluation on shifted subsets. Without such analysis, it is unclear whether the conservative regularizer alone suffices to bound overestimation when batching patterns, timing, or merchant/courier behavior change after deployment.

    Authors: We agree that the current manuscript lacks explicit quantification of distribution shift between the offline logged data and the policy-induced trajectories observed after deployment. The conservative regularizer was introduced specifically to mitigate overestimation under such shifts, and the production switchback experiment provides empirical evidence that the learned policy improved batching without degrading customer metrics. To strengthen the presentation, the revised manuscript will add (i) a quantitative comparison of state-action occupancy measures (e.g., via KL divergence or total variation on discretized state features) between the training logs and the post-deployment trajectories collected during the switchback, and (ii) off-policy value estimates on temporally or geographically shifted subsets of the logged data. These additions will clarify the degree of shift encountered and the extent to which the regularizer bounds overestimation in practice. revision: yes

Circularity Check

0 steps flagged

No circularity: offline RL training on logged data evaluated via independent live experiment

full rationale

The paper trains a policy via Double Q-learning plus conservative regularizer on centralized logged marketplace data, then deploys it for decentralized execution and validates via a separate production switchback experiment. No step equates a claimed prediction or result to its own fitted inputs by construction, no self-citation is load-bearing for the central claim, and the live experiment supplies external validation outside the training distribution. The derivation chain is therefore self-contained against the reported benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no information on free parameters, background axioms, or newly postulated entities.

pith-pipeline@v0.9.1-grok · 5717 in / 1165 out tokens · 22401 ms · 2026-06-27T06:45:37.875384+00:00 · methodology

discussion (0)

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