# Deep Reinforcement Learning for Resource Allocation in Business Processes

arXiv: https://arxiv.org/abs/2104.00541 · HTML: https://mlsanity.com/paper/2104.00541/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 29 Mar 2021.

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

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)

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

- [A Case for Business Process-Specific Foundation Models](https://mlsanity.com/paper/2210.14739/) (2022)
- [Learning policies for resource allocation in business processes](https://mlsanity.com/paper/2304.09970/) (2023)
- [A Rollout-Based Algorithm and Reward Function for Resource Allocation in Business Processes](https://mlsanity.com/paper/2504.11250/) (2025)
- [A Universal Approach to Feature Representation in Dynamic Task Assignment Problems](https://mlsanity.com/paper/2507.03579/) (2025)

## Abstract (arXiv)

Assigning resources in business processes execution is a repetitive task that can be effectively automated. However, different automation methods may give varying results that may not be optimal. Proper resource allocation is crucial as it may lead to significant cost reductions or increased effectiveness that results in increased revenues. In this work, we first propose a novel representation that allows modeling of a multi-process environment with different process-based rewards. These processes can share resources that differ in their eligibility. Then, we use double deep reinforcement learning to look for optimal resource allocation policy. We compare those results with two popular strategies that are widely used in the industry. Learning optimal policy through reinforcement learning requires frequent interactions with the environment, so we also designed and developed a simulation engine that can mimic real-world processes. The results obtained are promising. Deep reinforcement learning based resource allocation achieved significantly better results compared to two commonly used techniques.