# Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms

arXiv: https://arxiv.org/abs/2604.22797 · HTML: https://mlsanity.com/paper/2604.22797/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Closed-loop model-predictive wind farm flow control under time-varying inflow using FLORIDyn](https://mlsanity.com/paper/2503.02790/) (2025)

## Abstract (arXiv)

Wind farm wake steering optimization is challenging due to complex flow physics and changing conditions. This paper presents a hierarchical framework that combines reinforcement learning with model predictive control, where an RL agent learns compensatory state estimates for an MPC controller, rather than directly controlling turbines. Evaluated on a three-turbine case, the approach achieves a 23% power gain over the baseline control and surpasses the idealized MPC with perfect state knowledge. Compared to direct RL control, the hybrid architecture maintains superior safety characteristics during training while achieving comparable performance with more stable control actions.