Review of Deep Reinforcement Learning for Offshore Wind Farm Maintenance Planning

Borsotti, Marco; Jiang, Xiaoli; Negenborn, Rudy R.

doi:10.5194/wes-2025-222

Preprints

https://doi.org/10.5194/wes-2025-222

Preprints

07 Nov 2025

| 07 Nov 2025

Status: this preprint is currently under review for the journal WES.

Review of Deep Reinforcement Learning for Offshore Wind Farm Maintenance Planning

Marco Borsotti, Xiaoli Jiang, and Rudy R. Negenborn

Abstract. Offshore wind farms face unique challenges in maintenance due to harsh weather, remote locations, and complex logistics. Traditional maintenance strategies often fail to optimize operations, leading to unplanned failures or unnecessary servicing. In recent years, Deep Reinforcement Learning (DRL) has shown clear potential to tackle these challenges through a data-driven approach. This paper provides a critical review of representative DRL models for offshore wind farm maintenance planning, elaborating on both single- and multi-agent frameworks, diverse training algorithms, various problem formulations, and the integration of domain-specific knowledge. The review compares the benefits and limitations of these methods, identifying a significant gap in the widely adopted use of simplistic binary maintenance decisions, rather than including multi-level or imperfect repairs in the action space. This work finally suggests directions for future research, to overcome current limitations and enhance the applicability of DRL methods in offshore wind maintenance.

Received: 28 Oct 2025 – Discussion started: 07 Nov 2025

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Marco Borsotti, Xiaoli Jiang, and Rudy R. Negenborn

Status: final response (author comments only)

RC1:
'Comment on wes-2025-222', Anonymous Referee #1, 13 Nov 2025
The paper
“Review of Deep Reinforcement Learning for Offshore Wind Farm Maintenance Planning”,
By
Borsotti et al.,
provides a structured and timely overview of how DRL methods can optimise offshore wind operations and maintenance.
The survey spans single-agent, multi-agent, and hybrid formulations, and argues—persuasively—that binary “maintain vs not” actions limit realism, advocating multi-level repairs. It synthesises algorithmic families, problem formulations, and domain knowledge.
Overall, there are all the components for a good document and an effective contribution to the field. Nevertheless, while the paper’s clarity is commendable, its method is less so. The review work should methodically follow a rigorous process, e.g. thr PRISMA guidelines. Thus, the following remarks should be fully assessed before being reconsidered for acceptance
The review mentions a “deliberately narrowed” corpus but does not specify databases, search terms, or inclusion/exclusion criteria. A clear description of the literature selection protocol (possibly following, as much as possible, the PRISMA guidelines) is necessary to ensure reproducibility and reduce selection bias. “Narrow” should not mean “non-methodical”.

Several performance comparisons (e.g., “11.1% compared to a baseline schedule” or “11.1% compared to a baseline schedule.”) are reported with not enough consistent context. A dedicated table summarising datasets, baselines, metrics, and quantitative gains would allow readers to better assess the validity of these claims.

Section 4.2 introduces Partially Observable MDP (POMDP) but stops short of analysing practical remedies such as recurrent (DRQN/LSTM) or transformer-based policies. Expanding this discussion would highlight how belief-state estimation and memory mechanisms address uncertainty.

Section 9 “Multi-Level Maintenance Strategies in DRL Planning Models” very convincingly argues for richer maintenance actions, yet it remains conceptual. Including at least one illustrative wind-specific example (e.g., gearbox vs. blade repairs with cost/restoration factors), either real or synthetic, and/or citing an experimental demonstration, would make the argument more concrete.

While the authors acknowledge the dominance of simulation studies in the literature (which is a great problem, not only limited to WT applications), some claims of field applicability appear a little too optimistic. A more critical discussion on simulation-to-real transfer would better frame the maturity of DRL for actual offshore deployment.

Figures and Tables are informative but need more self-contained captions (especially Fig 5, 6 and Table 2). For instance, indicate the number of reviewed studies (N) behind each distribution and define classification criteria.

It seems that there are some duplicates in the Reference List; please double-check.

The text alternates between “POMDP” and “PO-MDP.” Please adopt a consistent notation throughout.

The Authors may consider mentioning related works on structural health monitoring and non-destructive testing for further context, see e.g. https://doi.org/10.3390/s22041627

There are a few typographical issues and potential mistakes (convective->convolutional?)

Again about following PRISMA guidelines, adding a short “Threats to validity” paragraph on publication bias, reproducibility, and generalisability (also beyond the offshore domain) would enhance the scientific rigour of the review.
Citation: https://doi.org/10.5194/wes-2025-222-RC1
RC2: 'Comment on wes-2025-222', Anonymous Referee #2, 05 Dec 2025

This manuscript reviews studies on the application of Deep Reinforcement Learning (DRL) for offshore wind-farm maintenance planning and highlights the potential benefits of DRL compared to traditional maintenance strategies. The topic of implementing data-driven approaches for operation and maintenance is highly relevant for the cost-effective deployment of offshore wind farms. Also, the overall structure of the manuscript is organized, with the abstract providing an informative summary of the content.
As stated in Line 474, “real-world applications of DRL in offshore wind O&M are still in early stages”, most of the studies reviewed rely solely on simulations. To provide a more balanced perspective, the manuscript should not only highlight the potential of DRL, but also acknowledges the limitations, assumptions, and simplifications inherent in these simulation-based approaches. Also, some discussions lack sufficient evidence and requires further clarification. The following comments are offered with the aim of improving the quality of the manuscript.

Citation: https://doi.org/10.5194/wes-2025-222-RC2

Marco Borsotti, Xiaoli Jiang, and Rudy R. Negenborn

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Short summary

This study explores how artificial intelligence can improve the maintenance of offshore wind farms. By reviewing recent research, we show that learning-based methods can predict failures, plan repairs more efficiently, and reduce costs compared to traditional strategies. We identify key gaps in current approaches and suggest ways to make these models more realistic and practical for real-world wind energy operations.


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