# To Start Up a Start-Up-Embedding Strategic Demand Development in Operational On-Demand Fulfillment via Reinforcement Learning with Information Shaping

arXiv: https://arxiv.org/abs/2504.05633 · HTML: https://mlsanity.com/paper/2504.05633/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 84 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.13653/) (2023)
- [Action Space Shaping in Deep Reinforcement Learning](https://mlsanity.com/paper/2004.00980/) (2020)
- [Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem](https://mlsanity.com/paper/1911.11260/) (2019)
- [Deep Q-Learning for Same-Day Delivery with Vehicles and Drones](https://mlsanity.com/paper/1910.11901/) (2019)
- [Approximate Dynamic Programming for Planning a Ride-Sharing System using Autonomous Fleets of Electric Vehicles](https://mlsanity.com/paper/1810.08124/) (2018)
- [Same-Day Delivery with Fairness](https://mlsanity.com/paper/2007.09541/) (2020)
- [A Deep Reinforcement Learning Approach for the Meal Delivery Problem](https://mlsanity.com/paper/2104.12000/) (2021)
- [Optimizing Warfarin Dosing using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.03486/) (2022)
- [Profit maximization via capacity control for distribution logistics problems](https://mlsanity.com/paper/2008.03216/) (2020)
- [Strategic Workforce Planning in Crowdsourced Delivery with Hybrid Driver Fleets](https://mlsanity.com/paper/2311.17935/) (2023)
- [An Explainable Deep Reinforcement Learning Model for Warfarin Maintenance Dosing Using Policy Distillation and Action Forging](https://mlsanity.com/paper/2404.17187/) (2024)

## Abstract (arXiv)

The last few years have witnessed rapid growth in the on-demand delivery market, with many start-ups entering the field. However, not all of these start-ups have succeeded due to various reasons, among others, not being able to establish a large enough customer base. In this paper, we address this problem that many on-demand transportation start-ups face: how to establish themselves in a new market. When starting, such companies often have limited fleet resources to serve demand across a city. Depending on the use of the fleet, varying service quality is observed in different areas of the city, and in turn, the service quality impacts the respective growth of demand in each area. Thus, operational fulfillment decisions drive the longer-term demand development. To integrate strategic demand development into real-time fulfillment operations, we propose a two-step approach. First, we derive analytical insights into optimal allocation decisions for a stylized problem. Second, we use these insights to shape the training data of a reinforcement learning strategy for operational real-time fulfillment. Our experiments demonstrate that combining operational efficiency with long-term strategic planning is highly advantageous. Further, we show that the careful shaping of training data is essential for the successful development of demand.