# DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries

arXiv: https://arxiv.org/abs/2311.02017 · HTML: https://mlsanity.com/paper/2311.02017/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 3 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers & Goods Transportation](https://mlsanity.com/paper/2007.13699/) (2020)
- [PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning](https://mlsanity.com/paper/2011.08999/) (2020)
- [PPtaxi: Non-stop Package Delivery via Multi-hop Ridesharing](https://mlsanity.com/paper/1809.04733/) (2018)

## Built on by (most cited first, 4 of 4)

- [A Coalition Game for On-demand Multi-modal 3D Automated Delivery System](https://mlsanity.com/paper/2412.17252/) (2024)
- [Altruistic Ride Sharing: A Framework for Fair and Sustainable Urban Mobility via Peer-to-Peer Incentives](https://mlsanity.com/paper/2510.13227/) (2025)
- [Multi-Agent Training-free Urban Food Delivery System using Resilient UMST Network](https://mlsanity.com/paper/2604.03280/) (2026)
- [Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity](https://mlsanity.com/paper/2605.27385/) (2026)

## Abstract (arXiv)

Delivery of items from the producer to the consumer has experienced significant growth over the past decade and has been greatly fueled by the recent pandemic. Amazon Fresh, Shopify, UberEats, InstaCart, and DoorDash are rapidly growing and are sharing the same business model of consumer items or food delivery. Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path. We observe a significant scope for reducing the costs associated with completing deliveries under the current model. We model our food delivery problem as a multi-objective optimization, where consumer satisfaction and delivery costs, both, need to be optimized. Taking inspiration from the success of ride-sharing in the taxi industry, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Unlike previous attempts for path-sharing, DeliverAI can provide real-time, time-efficient decision-making using a Reinforcement learning-enabled agent system. Our novel agent interaction scheme leverages path-sharing among deliveries to reduce the total distance traveled while keeping the delivery completion time under check. We generate and test our methodology vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 12%, the distance traveled by 13%, and achieve 50% higher fleet utilization compared to the baselines.