Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations
Reinforcement learning (RL) offers a promising approach for adaptive wind farm flow control, yet its practical deployment is hindered by slow training convergence and poor initial performance, factors that could translate to years of reduced power output if an untrained agent were deployed directly. This work investigates whether domain knowledge from steady-state wake models can accelerate RL training and improve initial controller performance. We propose a pretraining methodology in which expert demonstrations are generated by deploying a PyWake-based steady-state optimizer within a dynamic wake simulation (WindGym), then used to initialize both the actor and critic networks of a Soft A...
| Authors | Marcus Binder Nilsen, Julian Quick, Tuhfe Göçmen et al. |
|---|---|
| Primary category | eess.SY |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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