mlsanityData to 13 Oct 2026

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

arXiv 2402.17402 ↗First posted 27 Feb 2024physics.comp-ph cs.LG cs.SYcited by 3

On the map: Deep reinforcement learning methods → RL with diffusion and flow models

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.

Key facts. Cited by 3 papers in the map (data through 13 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.

Builds on

6 of 34 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 14,592
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,310
  3. 2020What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Studycited by 161
  4. 2019A review on Deep Reinforcement Learning for Fluid Mechanicscited by 65
  5. 2023Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applicationscited by 2
  6. 2019Funnel control for a moving water tankcited by 1

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Controlcited by 2
  2. 2026Heuristic Learning for Active Flow Control Using Coding Agentscited by 1
  3. 2025Dragonfly: a modular deep reinforcement learning librarycited by 0