# Reinforcement Learning for Assignment Problem with Time Constraints

arXiv: https://arxiv.org/abs/2106.02856 · HTML: https://mlsanity.com/paper/2106.02856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 5 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem](https://mlsanity.com/paper/1911.11260/) (2019)
- [ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems](https://mlsanity.com/paper/1911.10641/) (2019)
- [Predictive Image Regression for Longitudinal Studies with Missing Data](https://mlsanity.com/paper/1808.07553/) (2018)
- [Y-net: Biomedical Image Segmentation and Clustering](https://mlsanity.com/paper/2004.05698/) (2020)

## Built on by (most cited first, 1 of 1)

- [Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization](https://mlsanity.com/paper/2503.01069/) (2025)

## Abstract (arXiv)

We present an end-to-end framework for the Assignment Problem with multiple tasks mapped to a group of workers, using reinforcement learning while preserving many constraints. Tasks and workers have time constraints and there is a cost associated with assigning a worker to a task. Each worker can perform multiple tasks until it exhausts its allowed time units (capacity). We train a reinforcement learning agent to find near optimal solutions to the problem by minimizing total cost associated with the assignments while maintaining hard constraints. We use proximal policy optimization to optimize model parameters. The model generates a sequence of actions in real-time which correspond to task assignment to workers, without having to retrain for changes in the dynamic state of the environment. In our problem setting reward is computed as negative of the assignment cost. We also demonstrate our results on bin packing and capacitated vehicle routing problem, using the same framework. Our results outperform Google OR-Tools using MIP and CP-SAT solvers with large problem instances, in terms of solution quality and computation time.