# ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems

arXiv: https://arxiv.org/abs/1911.10641 · HTML: https://mlsanity.com/paper/1911.10641/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2019.

## Built on by (most cited first, 21 of 21)

- [RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark](https://mlsanity.com/paper/2306.17100/) (2023)
- [Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX](https://mlsanity.com/paper/2306.09884/) (2023)
- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Deep Inventory Management](https://mlsanity.com/paper/2210.03137/) (2022)
- [Analytics and Machine Learning in Vehicle Routing Research](https://mlsanity.com/paper/2102.10012/) (2021)
- [An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem](https://mlsanity.com/paper/2107.07076/) (2021)
- [Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems](https://mlsanity.com/paper/2105.12899/) (2021)
- [Neural Stochastic Dual Dynamic Programming](https://mlsanity.com/paper/2112.00874/) (2021)
- [Towards Automatic Actor-Critic Solutions to Continuous Control](https://mlsanity.com/paper/2106.08918/) (2021)
- [Learning an Inventory Control Policy with General Inventory Arrival Dynamics](https://mlsanity.com/paper/2310.17168/) (2023)
- [Where the Action is: Let's make Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problems work!](https://mlsanity.com/paper/2103.00507/) (2021)
- [K-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic Control](https://mlsanity.com/paper/2306.04836/) (2023)
- [Reinforcement Learning for Assignment Problem with Time Constraints](https://mlsanity.com/paper/2106.02856/) (2021)
- [Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness](https://mlsanity.com/paper/2507.14446/) (2025)
- [Actor-Accelerated Policy Dual Averaging for Reinforcement Learning in Continuous Action Spaces](https://mlsanity.com/paper/2603.10199/) (2026)
- [gym-invmgmt: An Open Benchmarking Framework for Inventory Management Methods](https://mlsanity.com/paper/2605.11355/) (2026)
- [Scheduling the NASA Deep Space Network with Deep Reinforcement Learning](https://mlsanity.com/paper/2102.05167/) (2021)
- [DRL-ORA: Distributional Reinforcement Learning with Online Risk Adaption](https://mlsanity.com/paper/2310.05179/) (2023)
- [Pattern based learning and optimisation through pricing for bin packing problem](https://mlsanity.com/paper/2409.04456/) (2024)
- [Multi-Agent Environments for Vehicle Routing Problems](https://mlsanity.com/paper/2411.14411/) (2024)
- [Deep Reinforcement Learning solution for pickup and delivery routing problems with time window and capacity constraints](https://mlsanity.com/paper/2608.14156/) (2026)

## Abstract (arXiv)

Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games. We build on this previous work by applying RL algorithms to a selection of canonical online stochastic optimization problems with a range of practical applications: Bin Packing, Newsvendor, and Vehicle Routing. While there is a nascent literature that applies RL to these problems, there are no commonly accepted benchmarks which can be used to compare proposed approaches rigorously in terms of performance, scale, or generalizability. This paper aims to fill that gap. For each problem we apply both standard approaches as well as newer RL algorithms and analyze results. In each case, the performance of the trained RL policy is competitive with or superior to the corresponding baselines, while not requiring much in the way of domain knowledge. This highlights the potential of RL in real-world dynamic resource allocation problems.