Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles

Chao Dong; Jiahao You; Qihui Wu; Zhu Han; Ziye Jia

REVIEW 3 major objections 4 minor 1 cited by

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles

T0 review · 3 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read A generative-AI-enhanced multi-agent reinforcement learning algorithm, GAI-HAPPO, is claimed to reduce total task execution time by 22.8% in a cooperative UAV-ground-station MEC system serving unmanned surface vehicles.

desk verdict Load-bearing queue update error in Eq. (20) invalidates the simulation results, so the 22.8% claim is not evidence; the architecture combination is real but needs a corrected model. read the letter →

arxiv 2502.08119 v1 pith:FUFZTUTU submitted 2025-02-12 cs.AI cs.RO

Jiahao You , Ziye Jia , Chao Dong , Qihui Wu , Zhu Han This is my paper · ORCID

classification cs.AIcs.RO

keywords unmannedsurfacevehiclesmulti-accessedgecomputingtaskoffloadingUAVtrajectoryplanningmulti-agentreinforcementlearningheterogeneousagentproximalpolicyoptimizationgenerativeadversarialnetworktransformer

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a generative-AI-enhanced multi-agent reinforcement learning algorithm, GAI-HAPPO, can jointly schedule task offloading and UAV flight trajectories so that unmanned surface vehicles in a maritime edge-computing network finish computation faster. The authors formulate the joint problem as a mixed-integer nonlinear program, then solve it with transformer-based actor networks and a GAN-stabilized critic inside the HAPPO training scheme. If the claim is right, the algorithm gives a ready method for coordinating USVs, UAVs, and ground stations under task and trajectory uncertainty, with a reported 22.8% performance improvement over four benchmarks.

What carries the argument

The central mechanism is the GAI-HAPPO training loop: heterogeneous agents (USVs deciding offloading fractions and UAVs deciding flight azimuth and distance) are updated sequentially under a clipped surrogate objective, following HAPPO. A transformer self-attention layer in the actor maps state inputs to actions, and a GAN-based critic replaces the usual value network, with a generator predicting state values and a discriminator distinguishing true from predicted values. The advantage function is computed by generalized advantage estimation and propagated through the sequential updates.

What would settle it

Recompute the simulated delays, rewards, and training curves after replacing the UAV queue update with one that preserves unfinished work, e.g., $Q^u_j(t+1)=\max\{0, Q^u_j(t) + \sum_i p_{i,j}(t)\beta_i(t)d_i(t) - \tau f^u_j\}$. If GAI-HAPPO no longer shows the reported 22.8% advantage over the same baselines, the performance claim rests on the current queue formulation rather than on the algorithm alone.

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Extended reading notes

Core claim

The paper's central claim is that GAI-HAPPO outperforms existing benchmark algorithms—HAA2C, HAPPO, GAN-HAPPO, and Transformer-HAPPO—in minimizing total task execution time for a cooperative UAV-and-ground-station MEC system serving USVs. The authors report that the proposed algorithm achieves the highest average reward with stable convergence after 2,500 episodes, consistently lower delays as the numbers of USVs and UAVs vary, and an overall 22.8% performance improvement. The improvement is attributed to the transformer's capacity to model complex state dependencies and the GAN-based critic's contribution to training stability.

Load-bearing premise

The load-bearing premise is that the UAV queue update in Eq. (20) correctly describes the backlog of tasks waiting on each UAV; as written it resets the backlog to zero whenever no USV offloads to that UAV and scales it by the number of offloading USVs otherwise, so the delays and rewards used in training and evaluation depend on this equation being an accurate model.

Editorial extensions

If this is right

If GAI-HAPPO works as reported, task offloading and UAV trajectory planning can be solved jointly in a single learned policy for USV-UAV-GS networks.
The algorithm's stable convergence after 2,500 episodes suggests it can be deployed in dynamic maritime environments where task arrivals and USV trajectories keep changing.
The reported 22.8% improvement over HAPPO means the generative components add practical value beyond the base heterogeneous-agent method.
The same training structure could be applied to other MEC settings where agents have heterogeneous action spaces, such as ground robots and aerial relays.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural test that the paper does not run is a component ablation: replacing the transformer actor or the GAN critic with plain MLPs under identical seeds would isolate which generative component causes the 22.8% gain.
The queue update in Eq. (20) multiplies the existing UAV queue by the offloading indicators, so the backlog resets whenever no USV offloads to that UAV; a corrected backlog equation could change the simulated delays and rewards, though the algorithmic contribution would still be testable.
Because the reward is a throughput-like ratio (data size divided by delay), the method targets latency per bit; applying it to energy or fairness objectives would require a new reward design but the same actor-critic machinery.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: the 22.8% improvement claim is not supported because the queue recursions in Eqs. (20)–(21) corrupt the simulated environment. That’s the main thing you should know.

The paper does have a real contribution at the architectural level: combining a transformer-based actor with a GAN-based critic inside the HAPPO sequential update scheme, applied to UAV/ground-station MEC for USVs. That specific combination is new as far as I can tell, and the ablation baselines (GAN-HAPPO, Transformer-HAPPO) are the right way to isolate the two components. The system model and problem formulation are standard MINLP fare, and the writing is clear enough.

The soft spot is not small. Eq. (20) defines Q^u_j(t+1) = max{0, sum_i p_{i,j}(t) Q^u_j(t) + beta_i(t)d_i(t) - tau f^u_j}. The existing backlog is multiplied by the number of offloading USVs, so the queue resets to zero whenever no USV offloads to that UAV. And beta_i(t)d_i(t) is added to every UAV queue regardless of whether USV i offloads there (or anywhere). Eq. (21) has the same structure for GSs. The queues feed directly into the delays in Eqs. (23)–(24), the total delay, and the reward in Eq. (33). So every transition and reward used to train GAI-HAPPO and the benchmarks is generated by a model that is not the one described in problem P0. That is load-bearing, not a cosmetic typo.

There is also no code, no seeds, and no error bars; the 22.8% figure appears in the abstract and conclusion but I couldn't find a statistical test or a direct point comparison in the figures. The binary offloading variables are placed in a continuous action space with no feasibility mechanism, which is a secondary concern.

On the citation pattern: they rely heavily on their own prior work, but those references are relevant and standard in this subfield, so I wouldn't call it a problem.

Who gets value from this? Readers working on MARL architectures for MEC might look at the actor-critic design, but the empirical validation is not usable in its current form. I would not send this to peer review as is — a desk reject with clear instructions to fix the queue recursions and rerun all simulations is the appropriate path. If the authors correct the model and the results still show an improvement, then it becomes a reasonable incremental paper.

Referee Report

3 major / 4 minor

Summary. The paper considers a maritime multi-access edge computing (MEC) scenario in which unmanned surface vehicles (USVs) generate computation tasks that can be processed locally, by UAVs, or by ground stations. It formulates a mixed-integer nonlinear program (MINLP) that jointly optimizes task offloading, workload splitting, and UAV trajectories to minimize total execution time, and then proposes GAI-HAPPO, a heterogeneous-agent proximal policy optimization algorithm that uses a transformer-based actor and a GAN-based critic. Simulations compare GAI-HAPPO with HAA2C, HAPPO, GAN-HAPPO, and Transformer-HAPPO, reporting a 22.8% improvement in average reward. The core of the paper is the system model in Section II and the algorithm design in Section III, followed by simulation results in Section IV.

Significance. If the proposed algorithm and simulation results were valid, the work would be a useful contribution to latency-aware task offloading for USV networks, and the combination of transformer actors and GAN critics within a HAPPO framework is a timely algorithmic idea. The paper also benefits from a reasonably broad benchmark comparison. However, the central queue dynamics in the system model are algebraically inconsistent, and this error propagates through the delay and reward functions that define the simulation environment. The reported performance gains therefore do not currently provide valid evidence for the proposed approach on the stated problem. The contribution is not established as presented, and the core issue would require a re-derivation of the model and a full re-running of the experiments.

major comments (3)

[Section II-D, Eq. (20)] The UAV queue recursion is algebraically inconsistent with the intended backlog model. The term \sum_i p_{i,j}(t) Q_j^u(t) multiplies the existing queue by the offloading indicator sum, so the backlog is erased whenever no USV offloads to UAV j in slot t, and it is scaled by the number of offloading USVs when several do. In addition, \beta_i(t)d_i(t) is summed over all i for every UAV j regardless of whether USV i offloads to j, so work that is not sent to that UAV still enters its queue. Equation (21) has the same defect for the ground-station queue. Because these queues enter the computation delays in Eqs. (23)-(24), the total delay in Eq. (25), and the reward in Eq. (33), every state transition and reward signal used in training and evaluation is generated by an environment that does not correspond to problem P0. The reported 22.8% improvement and the convergence behavior in Fig. 3 are therefore not evidence for GAI-HAPPO on the stated problem.
[Section III-A, Eq. (31)] The USV action space is defined as a vector of real-valued variables containing p_{i,j}, q_{i,k}, \alpha_i, \beta_i, and \gamma_i, but p and q are binary by Eqs. (14)-(15) and must satisfy the exclusive-offload constraints (16)-(17). The paper does not describe how continuous actions are mapped to feasible binary decisions, nor whether the constraints are enforced by masking, projection, or penalty. Without such a mechanism, the learned policy cannot be guaranteed to produce admissible offloading choices, and the comparison with benchmark algorithms is not well-defined.
[Section IV-B and IV-C] The simulation results reuse the corrupted queue environment for both GAI-HAPPO and all baselines, so the relative ordering of the algorithms may be an artifact of the erroneous dynamics. The paper claims a 22.8% improvement in the abstract, introduction, and conclusion, but Section IV never defines how this number is computed, which baseline it refers to, or which figure supports it. Furthermore, the training curves in Fig. 3 and the bar charts in Fig. 4 are presented without multiple seeds or error bars, making it impossible to assess statistical significance even if the environment equations were corrected.

minor comments (4)

[Section II-A, Eq. (1)] The path-loss formula contains a typo: the term "exp{−b[ςi,j(t) − a}" is missing a closing bracket, and the denominator "1 + a exp{...}" is ambiguous. Please correct the expression and ensure all brackets are balanced.
[Section III-A, action space] In the description after Eq. (31), the text says "the UAV needs to choose which UAV and GS to offload the tasks," but this action space is for the USV agent. This appears to be a typo and should be fixed.
[Section IV-A] The hyperparameter settings are incomplete: only learning rates are given (5×10^{-5} and 10^{-4} for actor and critic, presumably), but the PPO clip range, batch size, discount factor, and GAN training details are not specified. Please provide a complete table of parameters to support reproducibility.
[References] The introduction and related discussion rely heavily on the authors' prior works [2], [4], [6], and [14]. The novelty of the proposed framework relative to these prior contributions should be stated more explicitly in the introduction.

Circularity Check

0 steps flagged · score 1.0 of 10

No substantive circularity: the claimed 22.8% improvement is an empirical simulation result, not a fitted prediction or a self-citation chain.

full rationale

The paper's central assertion is that GAI-HAPPO achieves a 22.8% performance improvement over benchmarks (Abstract and Section V). This is an observed simulation outcome: the reward is defined as r(t) = sum_i d_i(t)/T_i^t(t), a direct inverse-delay objective, and no parameter is fitted to this reported improvement and then renamed a prediction. The proposed method modifies the actor and critic with transformer self-attention and a GAN loss (Eqs. 34-39); these are architectural choices tested against external baselines HAA2C/HAPPO [16] and two author-constructed ablations. The HAPPO backbone is cited to an external JMLR paper [16], not to the authors' own prior work. Self-citations [2], [4], [6], and [14] appear only for motivation, related work, and standard path-loss parameterization; none is a uniqueness theorem or a premise on which the performance claim logically depends. The ablation comparison is an evaluation design, not circular reasoning. A separate algebraic concern in the queue recursions of Eqs. (20)-(21), where the backlog is multiplied by offloading indicators and beta_i(t)d_i(t) is added without p_{i,j}(t), affects the correctness of the simulator and therefore the validity of the empirical evidence, but that is a modeling or implementation flaw rather than circularity: the conclusion is not equivalent to the premises by construction. Therefore no circular step is identified.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the system model in Section II and the learning formulation in Section III. The most significant assumptions are domain-specific mobility and task models from prior literature, plus two ad hoc modeling choices: continuous handling of binary offloading variables, and the erroneous queue update in Eq. (20). No new physical entities are introduced.

free parameters (3)

Actor learning rate = 5e-5
Set by hand in Section IV.A; not swept; performance of all algorithms may be sensitive to this choice.
Critic learning rate = 1e-4
Set by hand in Section IV.A; same rationale.
PPO clip range epsilon = not specified
Standard PPO hyperparameter; not reported, so exact replication is impossible.

assumptions (5)

domain assumption Task generation follows a Poisson distribution with mean arrival rate λ_i = 15
Section II states this. It is chosen for simulation and not validated against maritime traffic data.
domain assumption USV velocities follow the Gauss-Markov mobility model
Section II.B, Eq. (5). The parameters μ, v, σ are not specified in the simulation section, making the mobility pattern under-specified.
domain assumption The air-to-ground path loss model from [14] applies to the maritime USV-UAV link
Section II.A, Eq. (1). The model parameters ζL, ζNL, a, b are taken from literature for terrestrial environments; maritime propagation is not validated.
ad hoc to paper Binary offloading decisions can be represented by continuous actions in the MARL action space
Section III.A.3 defines the USV action space to include p_i,j(t), q_i,k(t), α, β, γ without specifying how the binary constraints (16)-(17) are enforced by the actor network.
ad hoc to paper UAV queue dynamics in Eq. (20) correctly model processing backlog
The equation is algebraically inconsistent: it multiplies Qu_j(t) by the sum of offloading indicators, causing queue resets when no offload occurs. This is the load-bearing modeling error.

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Cite this review

Pith. "Pith review of Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles." pith.science (2026). https://pith.science/paper/FUFZTUTU

@misc{pith2026250208119,
  author       = {Pith},
  title        = {Pith review of: Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FUFZTUTU}},
  note         = {Machine review of arXiv:2502.08119}
}

read the original abstract

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios.

Figures

Figures reproduced from arXiv: 2502.08119 by the authors.

**Figure 1.** System model overview. where G0 is the channel power gain when ∥l s i (t) − l g k (t)∥2 equals 1m [3]. Consequently, the transmission rate from USV i to GS k is R u2g i,k (t) = B u2g i,k log2 1 + P s i Gi,k(t) NG . (4) where B u2g i,k (t) represents the available bandwidth at GS k from USV i. B. USV Mobility Model The positions of USVs, UAVs, and GSs are depicted by a three-dimensional cartesian coordinate syste… view at source ↗

**Figure 3.** Average reward of standard setting. • GAN-HAPPO: Combines GAN with HAPPO to enhance the stability and accuracy of the critic network. • Transformer-HAPPO: Introduces transformer architectures into HAPPO to address sequential and spatialtemporal challenges in MARL. A. Parameter Setting We simulate a 1, 000m × 1, 000m area with 6 USVs generating tasks, supported by 4 UAVs and 2 GSs for task processing. Each USV gene… view at source ↗

**Figure 2.** The GAI-HAPPO framework in cooperative UAV-GS MEC. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Performance with different numbers of USVs and UAVs. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Continue with ORCID to comment.

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Reference graph

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Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

[1] [1]

A survey on mobility of edge computing networks in IoT: State-of-the-art, architectures, and challenges,

F. Shirin Abkenar, P. Ramezani, S. Iranmanesh, S. Murali, D. Chulert- tiyawong, X. Wan, A. Jamalipour, and R. Raad, “A survey on mobility of edge computing networks in IoT: State-of-the-art, architectures, and challenges,” IEEE Commun. Surv. Tutor ., vol. 24, no. 4, pp. 2329–2365, 4th Quart. 2022

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Z. Jia, J. You, C. Dong, Q. Wu, F. Zhou, D. Niyato, and Z. Han, “Cooperative cognitive dynamic system in UA V swarms: Reconfigurable mechanism and framework,” IEEE V eh. Technol. Mag. , vol. 19, no. 3, pp. 90–101, Jul. 2024

work page 2024

[3] [3]

Deep reinforcement learning based computation offloading and trajectory planning for multi-UA V cooperative target search,

Q. Luo, T. H. Luan, W. Shi, and P. Fan, “Deep reinforcement learning based computation offloading and trajectory planning for multi-UA V cooperative target search,” IEEE J. Sel. Areas Commun. , vol. 41, no. 2, pp. 504–520, Dec. 2023

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Y . Wu, Z. Jia, Q. Wu, and Z. Lu, “Adaptive QoE-aware SFC orchestra- tion in UA V networks: A deep reinforcement learning approach,” IEEE Trans. Netw. Sci. Eng. , vol. 11, no. 6, pp. 6052–6065, Nov. 2024

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[5] [5]

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Z. Yang, S. Bi, and Y .-J. A. Zhang, “Online trajectory and resource optimization for stochastic UA V-enabled MEC systems,” IEEE Trans. Wireless Commun., vol. 21, no. 7, pp. 5629–5643, Jul. 2022

work page 2022

[6] [6]

NFV-enabled service recovery in space-air-ground integrated networks: A matching game based approach,

Z. Jia, Y . Cao, L. He, G. Li, F. Zhou, Q. Wu, and Z. Han, “NFV-enabled service recovery in space-air-ground integrated networks: A matching game based approach,” IEEE Trans. Netw. Sci. Eng. , Feb. 2025, early access

work page 2025

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Collaborative multi-agent deep reinforce- ment learning for energy-efficient resource allocation in heterogeneous mobile edge computing networks,

Y . Xiao, Y . Song, and J. Liu, “Collaborative multi-agent deep reinforce- ment learning for energy-efficient resource allocation in heterogeneous mobile edge computing networks,” IEEE Trans. Wireless Commun. , vol. 23, no. 6, pp. 6653–6668, Jun. 2024

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[8] [8]

Integrating dynamic event-triggered and sensor-tolerant control: Application to USV-UA Vs cooperative formation system for maritime parallel search,

J. Li, G. Zhang, X. Zhang, and W. Zhang, “Integrating dynamic event-triggered and sensor-tolerant control: Application to USV-UA Vs cooperative formation system for maritime parallel search,” IEEE Trans. Intell. Transp. Syst. , vol. 25, no. 5, pp. 3986–3998, Nov. 2024

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