mlsanityData to 14 Oct 2026

Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions

arXiv 2609.12905 ↗First posted 11 Sep 2026cs.LGcited by 0

On the map: Offline RL and imitation learning from data → Offline RL algorithms

Abstract (arXiv)

This paper addresses the wind farm power maximization problem in the presence of wind direction changes. Specifically, a model-free Modified Twin Delayed Deep Deterministic Policy Gradient with Behavior Cloning (MTD3-BC) algorithm is proposed to tackle this task through yaw control under varying wind direction conditions. MTD3-BC is an offline reinforcement learning (RL) algorithm that aims to infer good behavior from only a precollected offline dataset. Additionally, to ensure smooth and moderate yaw adjustments, a new action consistency term is introduced into the policy optimization objective. Unlike online RL methods, MTD3-BC does not require extensive interactions with a wind farm simulator during training, significantly reducing computational costs and training time. A wind tunnel experiment is conducted to validate the effectiveness of the algorithm under varying wind directions. The results demonstrate that MTD3-BC successfully mitigates wake effects, delivering farm-level power gains of approximately 10% over the baseline greedy strategy and performance on par with a data-calibrated model-based wake-steering benchmark, while requiring no wake model and only a small fraction of the training cost of online RL. To our knowledge, this is the first time an offline RL wind farm control policy has been validated and demonstrated experimentally.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 5 of its 28 references that are in the map. First posted on arXiv 11 Sep 2026.

Builds on

5 of 28 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021A Minimalist Approach to Offline Reinforcement Learning12 Jun

Other references · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2022Collective wind farm operation based on a predictive model increases utility-scale energy productioncited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations
  2. 2025How to craft a deep reinforcement learning policy for wind farm flow control
  3. 2025Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
  4. 2025WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control
  5. 2024Reinforcement learning to maximise wind turbine energy generation
  6. 2024Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions
  7. 2025Control of a Twin Rotor using Twin Delayed Deep Deterministic Policy Gradient (TD3)
  8. 2026Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms