# Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management

arXiv: https://arxiv.org/abs/2207.03851 · HTML: https://mlsanity.com/paper/2207.03851/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 8 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)

## Built on by (most cited first, 2 of 2)

- [Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problem](https://mlsanity.com/paper/2503.17194/) (2025)
- [Offline Reinforcement Learning for Warehouse SLAM Throughput Control](https://mlsanity.com/paper/2606.23978/) (2026)

## Abstract (arXiv)

Warehouse Management Systems have been evolving and improving thanks to new Data Intelligence techniques. However, many current optimizations have been applied to specific cases or are in great need of manual interaction. Here is where Reinforcement Learning techniques come into play, providing automatization and adaptability to current optimization policies. In this paper, we present Storehouse, a customizable environment that generalizes the definition of warehouse simulations for Reinforcement Learning. We also validate this environment against state-of-the-art reinforcement learning algorithms and compare these results to human and random policies.