Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning

Hongbao Li; Kun Guo; Qihui Wu; Sijie He; Ziye Jia

REVIEW 5 major objections 5 minor 18 references

Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning

T0 review · 5 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A dual-layer UAV architecture—high-altitude relay plus low-altitude computing drones—steered by hierarchical soft actor-critic and priority-based scheduling, achieves higher task completion and system utility than fixed-trajectory…

desk verdict A coherent hierarchical SAC offloading design whose performance claims are not yet backed by the experiments as reported. read the letter →

arxiv 2507.05722 v1 pith:VZULN5IR submitted 2025-07-08 cs.LG

Hongbao Li , Ziye Jia , Sijie He , Kun Guo , Qihui Wu This is my paper · ORCID

classification cs.LG

keywords UAV-assistedvehicularedgecomputingtaskoffloadingdeepreinforcementlearningsoftactor-critictrajectoryplanningpriority-basedschedulingpartialhierarchicaldecisionmaking

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

The paper aims to establish that a two-tier aerial computing network can keep vehicular tasks on schedule under heavy, fast-changing traffic. A high-altitude UAV relays delay-tolerant tasks to idle roadside units or a base station, while low-altitude UAVs supply nearby computing capacity, and the two layers coordinate through a mixed continuous-and-discrete control scheme. The authors reformulate the joint delay-and-energy optimization as a Markov decision process and solve it with a hierarchical soft actor-critic algorithm that decides offloading ratios and drone trajectories, leaving node selection and CPU allocation to a priority-based rule. Reported simulations show the scheme keeping task completion near 95 percent at full relay bandwidth and about 87.5 percent when bandwidth drops to 40 MHz, while fixed-trajectory, no-priority, and DQN baselines decline more steeply.

What carries the argument

The load-bearing mechanism is a two-level action decomposition. The upper level is a soft actor-critic agent that observes a global state—vehicle positions, tasks, speeds, node locations and remaining compute, and LUA V motion—and outputs continuous actions: the per-vehicle offloading-ratio vector and LUA V headings and speeds. The lower level is a priority-based scheduler that orders tasks by priority and deadline, scores candidate nodes by distance and remaining resources, and requires a node to hold more than 30 percent free capacity before it can be selected. This decomposition turns a mixed-integer nonlinear program into a Markov decision process with a small continuous action space, which is what makes SAC training stable enough to compare favorably with the baselines.

What would settle it

Run the identical algorithm with delayed or partial state feedback and packet-level interference; if task completion at 40 MHz relay bandwidth falls below the 0.820 reported for the fixed-trajectory baseline, the claimed benefit of dynamic trajectories and ratio control is contradicted.

Watch

Extended reading notes

Core claim

The central claim, stated on the paper's own terms, is that decoupling global from local decisions is what makes UAV-assisted vehicular offloading both tractable and effective. The proposed dual-layer architecture has a high-altitude UAV (HUA V) act as relay and coordinator and low-altitude UAVs (LUA Vs) act as nearby compute nodes; the algorithm then splits the decision: a soft actor-critic agent outputs continuous offloading ratios and LUA V trajectories, and a priority-based scheduler picks target nodes and allocates their remaining CPUs. Against baselines that drop priority scheduling, freeze trajectories, or use a discrete-action deep Q-network, the reported experiments show the proposed method sustaining higher task completion rates as vehicle density scales from 5 to 50 and as relay bandwidth narrows, at a reported 0.950 completion rate at 100 MHz and 0.875 at 40 MHz.

Load-bearing premise

The plan assumes the high-altitude UAV receives complete, accurate, immediate knowledge of every vehicle's position, task, speed, and every node's remaining resources, and that radio links are only distance-based line-of-sight/non-line-of-sight rates with no interference or control overhead.

Editorial extensions

If this is right

If the central claim holds, operators can absorb rising vehicle density by adding low-altitude UAVs and letting the high-altitude relay re-dispatch them, since completion rate stays high from 5 to 50 vehicles in the reported runs.
Urgent tasks benefit most from the priority ordering, because deadlines and priority values determine scheduling order before resource allocation.
Deploying more LUA Vs raises completion rate but eventually stops raising system utility, since propulsion energy grows; the reported utility curves identify a deployment sweet spot rather than a monotone benefit.
The continuous SAC action space converges faster than the discrete DQN baseline in the reported episodes, suggesting that quantizing offloading ratios into discrete actions was a bottleneck for the baseline.
Under scarce relay bandwidth the dynamic trajectory and ratio control matter most: at 40 MHz the proposed method reports 0.875 completion while the fixed-trajectory baseline reports 0.820.

Reading between the lines

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

Beyond the paper, the obvious stress test is to replace the perfect global state assumption with noisy, delayed, or partial feedback; if completion gains shrink towards the baselines, the real-world value rests on the HUA V's sensing and reporting quality, not on the algorithm alone.
A second extension is to sweep the fixed 30-percent free-resource threshold in the scheduling rule; the reported gains could depend on that threshold matching the simulated load.
The model omits interference, control overhead, and sub-slot mobility, so a packet-level or ray-tracing simulator with concurrent transmissions would show how much of the reported 0.950 completion rate survives realistic radio conditions.
The same two-level decomposition—continuous global policy plus a rule-based local scheduler—could transfer to other heterogeneous edge settings with large mixed action spaces, such as ground base stations plus drone relays.

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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

Hi [Colleague], quick take on arXiv:2507.05722.

This is a straightforward, competently assembled paper on UAV-assisted vehicular edge computing. What's new is the specific combination: a high-altitude UAV relay with low-altitude computing UAVs, partial offloading, and a hierarchical SAC controller where the global policy outputs offloading ratios and trajectories while a priority-based scheduling layer does node selection and resource allocation. That exact package isn't in the cited literature, and the system model and problem formulation (Section II) are coherent, with consistent delay/energy equations and clear constraints. The SAC reformulation is standard but sensible, and the separation of global decisions from local scheduling is a reasonable way to handle the large action space.

The soft spot is the evaluation, not the model. All the performance claims rest on Figures 3-5, which show single learning curves with no error bars or seed counts. The baselines are ablations of the proposed system (NoPriority, FixedUAV, DQN), not established VEC benchmarks, so beating them doesn't establish state-of-the-art. The 'convergence speed' claim in the abstract and conclusion is never shown; there's no convergence curve comparing the proposed method against DQN or any other algorithm. Key parameters (normalization coefficients, the 30% resource threshold, scoring weights) are not reported, and no code or hyperparameters are provided. Even within the idealized model—perfect global state, distance-based LoS/NLoS channels, no interference—the evidence doesn't support the comparative claims as written. The stress-test note is on target.

One thing the reader's report flagged that I'd push back on: the reward being the same as the objective is standard practice in RL for constrained problems; that's not a flaw. The load-bearing issue is purely empirical.

Who is this for? Researchers working on UAV-assisted VEC who want a reference for a hierarchical control decomposition. The formulation has value. But as submitted, the performance claims should be treated with caution.

Recommendation: send to review. A serious referee can push the authors to add multiple seeds, error bars, hyperparameters, a convergence plot, and at least one non-ablation baseline. If that holds up, the paper would be a reasonable contribution. Without it, this is a conditional accept at best.

Referee Report

5 major / 5 minor

Summary. The manuscript proposes a dual-layer UAV-assisted vehicular edge computing architecture in which a high-altitude UAV (HUAV) relays traffic to RSUs or the BS while low-altitude UAVs (LUAVs) provide edge computation, with partial task offloading from vehicles. It formulates a mixed-integer nonlinear program P0 (Eq. 23a) that maximizes a weighted sum of task completion rate, negative delay, and negative energy consumption, then reformulates the problem as an MDP. A hierarchical SAC-based algorithm is introduced: the SAC agent outputs offloading ratios and LUAV trajectories, while a priority-based scheduler selects nodes and allocates computing resources. Simulation results in Sections IV and V claim that the proposed method outperforms NoPriority, FixedUAV, and DQN-based baselines in task completion rate, system utility, and convergence speed, with robustness to vehicle density and bandwidth variations.

Significance. If validated, the hierarchical decomposition of global continuous actions (offloading ratios and trajectories) plus a local priority scheduler would be a useful tool for managing the large action space in UAV-assisted VEC. The system model is coherent and includes propulsion energy for LUAVs and hovering energy for the HUAV, which is a positive feature. However, the performance claims are not currently established. The empirical evidence consists of single-seed, ablation-style baselines, the reward function duplicates the optimization objective, and the simulation environment assumes perfect global state and interference-free communication. No code, hyperparameters, or convergence plots are provided. The paper would make a modest contribution after a major revision that adds multi-seed experiments, stronger baselines or appropriately tempered claims, and full reproducibility details.

major comments (5)

[Section IV, Figs. 3-5] The central comparative claim rests on learning curves and bar-style comparisons without error bars, seed counts, or tabulated final values. The abstract and conclusion claim improved 'convergence speed,' but no figure or table compares the learning curve of the proposed SAC method against DQN-based or other baselines. Please provide multi-seed means with confidence intervals, tabulated steady-state metrics, and an explicit convergence-speed comparison (e.g., episodes to reach a target utility or reward).
[Section IV, baseline definitions] NoPriority, FixedUAV, and DQN-based are ablations of the proposed system or a discretized version of its own SAC logic; they are not established VEC offloading benchmarks. Therefore, the results can only show the contribution of each component, not that the method 'outperforms existing approaches' as stated in Section V. Please add comparisons with at least one or two published UAV-assisted or vehicular edge offloading algorithms (e.g., [9]-[13], [18]), or explicitly restrict the claim to outperforming the considered ablations.
[Section III-A, Eq. (26) and Section II-E, Eq. (23a)] The immediate reward r(t) is identical to the optimization objective P0 in Eq. (23a), with the task completion rate weighted by ω1 = 0.6. Because Rsucc is part of the training signal, the reported task completion-rate advantage partly reflects the chosen reward weights rather than an independent evaluation metric. This circularity is common in RL, but it should be stated explicitly, and the paper should report raw delay and energy components separately as well as the completion rate. In addition, robustness of the conclusions to ω1, ω2, and ω3 should be demonstrated.
[Section II-C and Section III-A, Eq. (24)] The simulation environment assumes perfect global state information (Eq. 24), quasi-static node positions within each time slot, and distance-based LoS/NLoS rates without interference, control overhead, or feedback delay. Consequently, the claim of 'strong robustness and adaptability in dynamic vehicular environments' in the abstract and Section IV is not actually demonstrated. Please include sensitivity analysis to imperfect state information, channel fading/shadowing dynamics, or mobility within a slot, or soften the robustness claim to the specific simulation conditions.
[Section IV and Section III-B, Eqs. (23a), (26), (28)] Key algorithmic parameters are missing: the normalization constants β_T and β_E in Eqs. (23a) and (26), the scoring weights α_s and β_s in Eq. (28), the 30% resource-availability threshold, and the SAC hyperparameters (learning rates, network sizes, batch sizes, entropy coefficient schedule). Without these values, the simulation results cannot be reproduced or meaningfully compared with other algorithms. Please provide a complete parameter table.

minor comments (5)

[Section II-D, Eq. (16)] There is a typo in the last term of Eq. (16): 'f_i(t)x' should read 'f_i^x(t)' to match the notation used elsewhere.
[Section IV, first paragraph] The sentence 'The simulations are implemented on the PyCharm platform' is imprecise, since PyCharm is an integrated development environment, not a simulation platform. Please rephrase as, for example, 'simulations are implemented in Python 3.8 and PyTorch.'
[Fig. 3 caption and text] The caption says 'System utility versus episode' while the text describes both task completion rate and system utility. The figure should be split into two clearly labeled subplots (one for completion rate, one for utility) with axis labels and units.
[Section IV, simulation setup] The simulation setup is described in prose without a consolidated parameter table. A table listing all default values (β0, α1, α2, Pn, bandwidths, transmit powers, node counts, energy model constants) would greatly improve readability and reproducibility.
[Eq. (23a)] The notation 'P0 : max_{λ,F,Q} = ...' is incomplete; it should be 'P0 : max_{λ,F,Q} ...' or a utility function should be defined on the right-hand side, such as 'maximize U(λ,F,Q) = ...'.

Circularity Check

1 steps flagged · score 3.0 of 10

The reported task completion rate and system utility are, by construction, the same weighted sum the SAC agent is trained to maximize; no other load-bearing circularity was found.

self definitional [Section III-A, Eq. (26); Section II-E, Eq. (23a); Section IV simulation settings]
"P0 : max = ω1Rsucc − ω2βT ∑_{i∈I} T_total_i(t) − ω3βEEsys(t) (23a) ... r(t) = ω1Rsucc(t) − ω2βT T_total(t) − ω3βEEsys(t), (26) ... the system utility is defined in accordance with the reward function, with the weights set as ω1 = 0.6, ω2 = 0.2, and ω3 = 0.2."

The reward in Eq. (26) is exactly the objective P0 in Eq. (23a): T_total(t) is defined as Σ_i T_total_i(t), so the reward and the optimization objective are the same weighted sum. The paper then defines its reported 'system utility' as this very reward with the same weights, and plots task completion rate, the ω1 = 0.6 component of the same quantity, as a headline metric. Hence the claimed gains in completion rate and system utility are the quantity the SAC agent is trained to maximize, not an independent performance measure. The comparison is not vacuous because all baselines are measured on the same reward, but the evaluation is self-referential by construction.

full rationale

The main derivation chain — communication and computation models, delay/energy equations, P0, the MDP reformulation, and the SAC update — is self-contained and does not import a load-bearing result from the authors' prior work. The self-citations (e.g., [3], [6]-[8], [12], [18]) supply contextual architecture and motivation, not an invoked uniqueness theorem or a forbidden ansatz, so they do not raise the circularity score. The one genuine circular element is the evaluation metric: Eq. (26) is the same objective as Eq. (23a), and Section IV explicitly adopts the reward as the system utility. Consequently, the reported completion-rate and utility curves are the training objective itself, which makes the 'demonstration' of superiority partially tautological. This is mitigated by the fact that all baselines are optimized under the same reward, so the relative ordering still has algorithmic content. Separately, the claimed convergence-speed advantage is asserted in the abstract and conclusion but never shown in a convergence plot; that is an evidence gap, not a circularity. Overall score 3 reflects one mild self-referential step in an otherwise non-circular derivation.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claim rests on a quasi-static, interference-free simulation model, a fully observable global state, and several hand-set parameters in the scheduling layer. No new physical entities are introduced; HUA V, LUA V, RSU, and BS are standard system components. The absence of parameter values for beta_T, beta_E, alpha_s, beta_s and the priority function makes the reported performance hard to reproduce.

free parameters (5)

Objective and reward weights omega_1, omega_2, omega_3 = 0.6, 0.2, 0.2
Set by hand in Section IV. These weights define the reward and system utility, so the reported completion rate and utility depend directly on this choice.
Normalization parameters beta_T, beta_E = not reported
Appear in objective P0 (Eq. 23a) and reward (Eq. 26) but no values are given; the delay and energy terms cannot be scaled without them.
Node score weights alpha_s, beta_s = not reported
Tunable parameters in Eq. (28) control candidate node selection in the priority scheduler; their values are never given, so scheduling behavior is not reproducible.
Resource availability threshold for candidate nodes = 30 percent
Section III-B requires candidate nodes to have more than 30 percent of resources free; this arbitrary threshold is not varied or justified.
Task priority function K_i(t) = unspecified
Section II-B describes priority qualitatively as resource intensity and delay sensitivity but gives no formula or distribution, leaving the priority sorting in Section III-B underspecified.

assumptions (6)

domain assumption Within each time slot, node positions are static.
Section II-A states 'Within each slot t, the node positions are assumed to be static', removing intra-slot mobility dynamics and simplifying the MDP state updates.
domain assumption Vehicle-to-RSU links are NLoS with log-normal shadowing; vehicle-to-UAV and HUA V links are LoS.
Section II-C fixes channel gains as distance-based LoS/NLoS formulas (Eqs. 1, 3, 5, 6) and omits interference and blockage dynamics, so all rates are deterministic functions of distance.
domain assumption Every vehicle generates exactly one computation task per time slot.
Section II-B defines a task tau_i(t) for each vehicle in each slot with parameters drawn from fixed ranges; no task arrival stochasticity or empty slots are modeled.
domain assumption Task execution delays across offloading modes are concurrent, so total delay is the max over modes and energy is additive.
Eqs. (18) and (20) in Section II-E define total delay as max over modes and energy as sum over modes; this modeling choice underlies the completion-rate objective.
domain assumption The HUA V agent observes the full network state each slot.
Eq. (24) in Section III-A defines state s(t) as positions, tasks, velocities, residual resources, and LUA V motion states for all nodes, assuming perfect global information and negligible control overhead.
ad hoc to paper A node is a candidate for offloading only if more than 30 percent of its computing resources remain available.
Section III-B imposes the 30 percent threshold without justification or sensitivity analysis; it directly changes which nodes can be selected and therefore the algorithm's behavior.

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Cite this review

Pith. "Pith review of Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning." pith.science (2026). https://pith.science/paper/VZULN5IR

@misc{pith2026250705722,
  author       = {Pith},
  title        = {Pith review of: Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VZULN5IR}},
  note         = {Machine review of arXiv:2507.05722}
}

read the original abstract

With the emergence of compute-intensive and delay-sensitive applications in vehicular networks, unmanned aerial vehicles (UAVs) have emerged as a promising complement for vehicular edge computing due to the high mobility and flexible deployment. However, the existing UAV-assisted offloading strategies are insufficient in coordinating heterogeneous computing resources and adapting to dynamic network conditions. Hence, this paper proposes a dual-layer UAV-assisted edge computing architecture based on partial offloading, composed of the relay capability of high-altitude UAVs and the computing support of low-altitude UAVs. The proposed architecture enables efficient integration and coordination of heterogeneous resources. A joint optimization problem is formulated to minimize the system delay and energy consumption while ensuring the task completion rate. To solve the high-dimensional decision problem, we reformulate the problem as a Markov decision process and propose a hierarchical offloading scheme based on the soft actor-critic algorithm. The method decouples global and local decisions, where the global decisions integrate offloading ratios and trajectory planning into continuous actions, while the local scheduling is handled via designing a priority-based mechanism. Simulations are conducted and demonstrate that the proposed approach outperforms several baselines in task completion rate, system efficiency, and convergence speed, showing strong robustness and applicability in dynamic vehicular environments.

Figures

Figures reproduced from arXiv: 2507.05722 by the authors.

**Figure 2.** The architecture of the proposed hierarchical offloa [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** System utility versus episode under different LUAV n [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Task completion rate comparison under varying vehic [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Task completion rate versus bandwidth allocation (2 [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

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Flexedge: Digital twin- enabled task ofﬂoading for UA V-aided vehicular edge comput ing,

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Z. Jia, Q. Wu, C. Dong, C. Y uen, and Z. Han, “Hierarchical aerial computing for Internet of things via cooperation of HAPs and UA Vs,” IEEE Internet Things J. , vol. 10, no. 7, pp. 5676–5688, Apr. 2023

work page 2023

[4] [1]

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J. Hu, C. Chen, T. Qiu, and Q. Pei, “Regional-centralized content dissemination for eV2X services in 5G mmwave-enabled IoV,” IEEE Internet of Things Journal , vol. 7, no. 8, pp. 7234–7249, Aug. 2020

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Generative AI-e nhanced cooperative MEC of UA Vs and ground stations for unmanned sur face vehicles,

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