# WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control

arXiv: https://arxiv.org/abs/2501.13592 · HTML: https://mlsanity.com/paper/2501.13592/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?](https://mlsanity.com/paper/2011.09533/) (2020)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [Fleet Control using Coregionalized Gaussian Process Policy Iteration](https://mlsanity.com/paper/1911.10121/) (2019)
- [Scalable Optimization for Wind Farm Control using Coordination Graphs](https://mlsanity.com/paper/2101.07844/) (2021)
- [An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning](https://mlsanity.com/paper/2305.01299/) (2023)

## Built on by (most cited first, 7 of 7)

- [AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance](https://mlsanity.com/paper/2506.03828/) (2025)
- [Closed-Loop Vision-Language Planning for Multi-Agent Coordination](https://mlsanity.com/paper/2502.10148/) (2025)
- [Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control](https://mlsanity.com/paper/2506.20554/) (2025)
- [Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review](https://mlsanity.com/paper/2507.10142/) (2025)
- [Scaling Multi-Agent Environment Co-Design with Diffusion Models](https://mlsanity.com/paper/2511.03100/) (2025)
- [DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules](https://mlsanity.com/paper/2605.08614/) (2026)
- [Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents](https://mlsanity.com/paper/2607.22563/) (2026)

## Abstract (arXiv)

The wind farm control problem is challenging, since conventional model-based control strategies require tractable models of complex aerodynamical interactions between the turbines and suffer from the curse of dimension when the number of turbines increases. Recently, model-free and multi-agent reinforcement learning approaches have been used to address this challenge. In this article, we introduce WFCRL (Wind Farm Control with Reinforcement Learning), the first open suite of multi-agent reinforcement learning environments for the wind farm control problem. WFCRL frames a cooperative Multi-Agent Reinforcement Learning (MARL) problem: each turbine is an agent and can learn to adjust its yaw, pitch or torque to maximize the common objective (e.g. the total power production of the farm). WFCRL also offers turbine load observations that will allow to optimize the farm performance while limiting turbine structural damages. Interfaces with two state-of-the-art farm simulators are implemented in WFCRL: a static simulator (FLORIS) and a dynamic simulator (FAST.Farm). For each simulator, 10 wind layouts are provided, including 5 real wind farms. Two state-of-the-art online MARL algorithms are implemented to illustrate the scaling challenges. As learning online on FAST.Farm is highly time-consuming, WFCRL offers the possibility of designing transfer learning strategies from FLORIS to FAST.Farm.