# Beacon, a lightweight deep reinforcement learning benchmark library for flow control

arXiv: https://arxiv.org/abs/2402.17402 · HTML: https://mlsanity.com/paper/2402.17402/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL with diffusion and flow models, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [RL with diffusion and flow models](https://mlsanity.com/deeprl/rl-with-diffusion-and-flow-models/)

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study](https://mlsanity.com/paper/2006.05990/) (2020)
- [A review on Deep Reinforcement Learning for Fluid Mechanics](https://mlsanity.com/paper/1908.04127/) (2019)
- [Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applications](https://mlsanity.com/paper/2304.12330/) (2023)
- [Funnel control for a moving water tank](https://mlsanity.com/paper/1902.00586/) (2019)

## Built on by (most cited first, 3 of 3)

- [Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control](https://mlsanity.com/paper/2601.15015/) (2026)
- [Heuristic Learning for Active Flow Control Using Coding Agents](https://mlsanity.com/paper/2607.11565/) (2026)
- [Dragonfly: a modular deep reinforcement learning library](https://mlsanity.com/paper/2505.03778/) (2025)

## Abstract (arXiv)

Recently, the increasing use of deep reinforcement learning for flow control problems has led to a new area of research, focused on the coupling and the adaptation of the existing algorithms to the control of numerical fluid dynamics environments. Although still in its infancy, the field has seen multiple successes in a short time span, and its fast development pace can certainly be partly imparted to the open-source effort that drives the expansion of the community. Yet, this emerging domain still misses a common ground to (i) ensure the reproducibility of the results, and (ii) offer a proper ad-hoc benchmarking basis. To this end, we propose Beacon, an open-source benchmark library composed of seven lightweight 1D and 2D flow control problems with various characteristics, action and observation space characteristics, and CPU requirements. In this contribution, the seven considered problems are described, and reference control solutions are provided. The sources for the following work are available at https://github.com/jviquerat/beacon.