# Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control

arXiv: https://arxiv.org/abs/2506.20554 · HTML: https://mlsanity.com/paper/2506.20554/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 79 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [TorchRL: A data-driven decision-making library for PyTorch](https://mlsanity.com/paper/2306.00577/) (2023)
- [Grasping Extreme Aerodynamics on a Low-Dimensional Manifold](https://mlsanity.com/paper/2305.08024/) (2023)
- [Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation](https://mlsanity.com/paper/2109.00525/) (2021)
- [WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control](https://mlsanity.com/paper/2501.13592/) (2025)
- [Collective wind farm operation based on a predictive model increases utility-scale energy production](https://mlsanity.com/paper/2202.06683/) (2022)
- [Map-based Experience Replay: A Memory-Efficient Solution to Catastrophic Forgetting in Reinforcement Learning](https://mlsanity.com/paper/2305.02054/) (2023)
- [The helix approach: using dynamic individual pitch control to enhance wake mixing in wind farms](https://mlsanity.com/paper/1912.10025/) (2019)

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

- [FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models](https://mlsanity.com/paper/2508.14315/) (2025)
- [Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control](https://mlsanity.com/paper/2604.08750/) (2026)

## Abstract (arXiv)

Traditional wind farm control operates each turbine independently to maximize individual power output. However, coordinated wake steering across the entire farm can substantially increase the combined wind farm energy production. Although dynamic closed-loop control has proven effective in flow control applications, wind farm optimization has relied primarily on static, low-fidelity simulators that ignore critical turbulent flow dynamics. In this work, we present the first reinforcement learning (RL) controller integrated directly with high-fidelity large-eddy simulation (LES), enabling real-time response to atmospheric turbulence through collaborative, dynamic control strategies. Our RL controller achieves a 4.30% increase in wind farm power output compared to baseline operation, nearly doubling the 2.19% gain from static optimal yaw control obtained through Bayesian optimization. These results establish dynamic flow-responsive control as a transformative approach to wind farm optimization, with direct implications for accelerating renewable energy deployment to net-zero targets.