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REVIEW 4 major objections 5 minor 133 references

Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures

T0 review · 4 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read This survey argues that DRL-based offloading in vehicular edge computing sorts into centralized, distributed, and hierarchical architectures, and that the field's recurring bottlenecks are incomplete MDP state representations…

desk verdict A useful survey map of DRL-based offloading in vehicular edge computing, but the internal classification inconsistencies and the unfulfilled MDP analysis claim make it a resource to use with caution. read the letter →

arxiv 2502.06963 v3 pith:CQRQMMLD submitted 2025-02-10 cs.LG cs.AIcs.DCcs.MA

Ashab Uddin , Ahmed Hamdi Sakr , Ning Zhang This is my paper · ORCID
classification cs.LGcs.AIcs.DCcs.MA
keywords vehicularedgecomputingdeepreinforcementlearningcomputationoffloadingMarkovdecisionprocessmulti-agentrewardfunctiondesigncentralizeddistributedhierarchicalarchitecturesintelligenttransportationsystems
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 is a survey that tries to establish that deep reinforcement learning (DRL) has become the main adaptive tool for deciding where vehicles send computational tasks, and that the resulting literature is best organized into centralized, distributed, and hierarchical offloading architectures. It claims that across all three architectures the same recurring defects appear: state representations that omit important dynamics, reward functions that are miscalibrated for multi-objective goals, and evaluations that lack proper DRL baselines. A sympathetic reader would take the intended contribution as a map of the field's design space plus a diagnosis of what blocks real deployment. If the diagnosis holds, future progress depends less on inventing new DRL algorithms and more on making MDP formulations complete, rewards well scaled, and comparisons honest.

What carries the argument

The central organizing device is the Markov Decision Process (MDP), the tuple $(S,A,P,R,\gamma)$ that defines state, action, transition, reward, and discount. The survey uses MDP/POMDP formulation as a lens to classify each offloading scheme and to expose omissions: which dynamics are left out of the state, how the reward weights competing objectives, and whether multi-agent learning assumes centralized training or decentralized execution. The architecture taxonomy (centralized, distributed, hierarchical) is the second load-bearing structure; it tells readers what kind of coordination problem each paper solves.

What would settle it

A reader could settle the core claim by taking the papers the survey places in each architecture and re-coding each one's state variables, reward terms, and baseline comparisons against a fixed checklist; if the alleged omissions, such as missing transmission energy, missing channel dynamics, or absent DRL baselines, appear in a different pattern or mostly disappear, the survey's recurring-limitation thesis would fail.

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Extended reading notes

Core claim

On its own terms, the survey's central claim is that DRL-based offloading in vehicular edge computing matures through three architectural paradigms, each with its own MDP structure: centralized offloading with a single agent or multiple agents reporting to one server; distributed offloading where multiple edge servers learn jointly, often under centralized training with decentralized execution; and hierarchical offloading across vehicles, edge, fog, UAVs, and cloud. It further claims that the field's open problems are not primarily algorithmic novelty but MDP completeness, reward-function calibration, baseline comparison, synchronization, and scalability. The evidence for this claim is a structured reading of roughly 130 studies, organized into tables that pair each method with its DRL algorithm, optimization objective, and computing source, followed by a section-by-section list of limitations that recur across independent works.

Load-bearing premise

The load-bearing premise is that the roughly 130 papers the survey selected and sorted into centralized, distributed, and hierarchical architectures fairly represent the field, so its lessons about MDP completeness, reward design, and evaluation gaps generalize.

Editorial extensions

If this is right

  • If the survey's diagnosis is right, proposed offloading policies should be evaluated on whether their state includes transmission energy, channel dynamics, mobility, queue length, and caching or task dependencies.
  • Reward design should move from raw or squared latency and energy terms to scaled, weighted, windowed objectives so that no single metric dominates learning.
  • Performance claims need consistent DRL baselines, including multi-agent baselines, plus QoS and QoE metrics rather than utility-only curves.
  • Multi-agent systems should adopt synchronization and coordination mechanisms, and new agents should be able to join without full retraining.
  • Deployment-focused work should address communication delays, computational constraints, and high-dimensional action spaces, with lightweight or generalized models.

Reading between the lines

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

  • If the recurring-limitation pattern is real, many reported gains over weak baselines may shrink when compared against a strong, fully informed MDP baseline, so public benchmark suites with fixed state and reward definitions would be the natural next step.
  • The survey's emphasis on incomplete state representations suggests that offline RL or model-based RL, which can leverage logged data from real vehicular systems, might progress faster than simulation-only DRL.
  • Because most surveyed studies rely on small synthetic simulations, hardware-in-the-loop or field trials could overturn specific performance claims even if the taxonomy survives.
  • Co-adaptive game-theory-plus-DRL designs, where policies update payoff models and equilibria co-evolve, are a plausible direction the survey names as open rather than a proven remedy.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. This survey reviews deep reinforcement learning (DRL) approaches to computational offloading in vehicular edge computing (VEC). It organizes roughly 133 cited papers into three architectural categories—centralized, distributed, and hierarchical—and, for each, summarizes DRL methods, optimization objectives, key techniques, and reported limitations. The paper also provides background on MDPs, DRL algorithms, and multi-agent RL, and ends with lessons learned and open research directions centered on incomplete MDP representations, miscalibrated reward functions, weak baselines, synchronization issues, and scalability.

Significance. If the survey's classifications and aggregate lessons are accurate, it provides a useful structured map of an active and crowded area, and its emphasis on MDP completeness, reward scaling, and evaluation practice is a plausible synthesis of recurring problems. The paper's strengths are its breadth of coverage, its per-architecture limitation subsections, and its attention to action-space and coordination issues that are often treated superficially in primary papers. However, the contribution is currently weakened by the absence of a disclosed literature-search methodology, by internal inconsistencies in the coding of individual papers, and by the fact that the promised systematic MDP analysis is not actually presented. The paper does not ship code or machine-checked artifacts, so its value rests entirely on the reliability of its synthesis.

major comments (4)
  1. [Abstract and §1.3] The paper claims to be a 'comprehensive review' and to 'systematically investigate' DRL-based offloading, but it never specifies the search strategy: no databases, keywords, inclusion/exclusion criteria, date range, or screening process are given. Since Section 8's lessons are aggregate statements over the selected 133 papers, the representativeness of that corpus is load-bearing. Without this methodology, the reader cannot distinguish a systematic survey from a convenience sample.
  2. [Tables 5–7, §5.3, §6.3] The coding of individual papers is internally inconsistent, which directly threatens the taxonomy on which the survey's conclusions rest. In Table 5, reference [75] is classified as 'MATD3 (DTDE)', yet §5.3 states that PPO is applied in [75] and that MADDPG and MAPPO are used in [73, 75]. Reference [76] appears in Table 5 as a centralized MAAC (DTDE) scheme, but §6.3 lists [76] among 'fully decentralized variants of MADQN and MAAC'. Additionally, the same critique of [68] appears in both §5.5.1 and §6.5.1, despite [68] being coded as a centralized scheme. The authors should state explicit, mutually exclusive classification criteria for 'centralized', 'distributed', and 'hierarchical', and re-code all entries consistently; otherwise the aggregate lessons in Section 8 cannot be trusted.
  3. [Abstract and §1.3 vs. Tables 5–7 and §8.1] The paper promises an analysis of MDP formulations—'we analyze how Markov Decision Process (MDP) formulations are applied'—and a contribution bullet claims to 'assess the variability and limitations of reward function design' and 'examine the role of action space representation'. However, no systematic coding of state, action, transition, and reward components across the surveyed papers is provided. Tables 5–7 list DRL method, optimization objective, key technique, and computing source, but not the MDP tuple elements. Consequently, statements such as those in §8.1 about 'incomplete state representations' are qualitative assertions rather than results of a documented cross-corpus analysis. A dedicated MDP-coding table or appendix is needed to substantiate the paper's central claims.
  4. [§5.5, §6.5, §7.5] The per-paper critiques that motivate the survey's open-problem lessons are not verifiable from the manuscript. For example, §5.5.1 says [69] 'fails to incorporate task and resource dynamics into the state representation', and §7.5.1 says [108] 'neglected to include task dynamics, resource dynamics, and channel variability', but the authors do not quote the original state vectors, reward equations, or experimental configurations. Since these critiques are the basis for the survey's conclusions about incomplete MDPs and miscalibrated rewards, the authors should cite specific sections or equations from the original papers, or provide a per-paper MDP coding matrix that allows the reader to check each judgment.
minor comments (5)
  1. [§1.2] The sentence beginning 'Focusing on One of the key challenges that arise in vehicular networks...' is grammatically incomplete, and nearby phrasing such as 'Surveys such as those [16] and [17] emphasized' and 'the work of [7]' is awkward. Please revise this paragraph for clarity.
  2. [Figures 7 and 8] The captions of Figures 7 and 8 contain the typo 'Hierarchial'; this should be 'Hierarchical'.
  3. [§6.5.3] The text calls MAPPO a deterministic policy, but PPO and its multi-agent variant are stochastic on-policy methods, as correctly stated in Section 4.3.3 and Table 3. Please correct this inconsistency.
  4. [§4.4.3] The MAAC algorithm is cited to [62], which is Zhang et al.'s 'Fully Decentralized Multi-Agent Reinforcement Learning With Networked Agents'; this reference does not describe the MAAC (multi-agent actor-critic with a centralized critic conditioned on other agents' actions) as the term is used in the MARL literature. Please update the citation.
  5. [Table 3] DQN, Double DQN, and Dueling DQN are labeled 'Deterministic' in the policy column. While the learned greedy policy is deterministic, the behavior policy during exploration is stochastic (e.g., epsilon-greedy). Consider clarifying this distinction in the table or its notes.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the survey organizes existing work into a taxonomy and draws aggregate lessons; these are summaries, not predictions or derivations from fitted inputs.

full rationale

The paper is a literature survey and does not derive a mathematical result from its inputs. It classifies 133 DRL offloading papers into centralized, distributed, and hierarchical architectures and then summarizes recurring limitations such as incomplete MDP states, miscalibrated rewards, and weak baselines. These lessons are cross-paper aggregations rather than quantities predicted from fitted parameters, so none of the enumerated circularity patterns apply. The only self-citation is reference [81] (Uddin, Sakr, and Zhang), used in Section 6.2 as one example of task-adaptive framing and prioritization and listed in Table 6. The taxonomy and the Section 8 conclusions do not depend on that reference; they are supported by many independent citations, so the self-citation is not load-bearing. The internal inconsistencies noted in the manuscript, such as [75] being described as PPO and CTDE in Section 5.3 while Table 5 lists it as MATD3 with DTDE, or [76] being centralized in Table 5 but called fully decentralized in Section 6.3, are correctness and reproducibility concerns about the survey's coding of the literature, not circularity. No claimed derivation in the paper reduces to its own inputs by construction, so no circular step can be identified and the score is minimal.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

This is a review, so the ledger of fitted parameters and invented entities is empty. The paper's conclusions depend on two unverified premises: that the cited papers are accurately described, and that the selected corpus is representative of the field. Both are standard assumptions for surveys but unstated here.

assumptions (2)
  • domain assumption The descriptions of the cited works in the tables and text accurately reflect the content of those papers.
    The survey's classifications and critiques (e.g., Tables 5-7, Sections 5.5, 6.5, 7.5) rely on the authors' reading of 133 external papers, which cannot be independently verified from this manuscript.
  • domain assumption The literature selected is representative of DRL-based offloading in VEC.
    The paper provides no search strategy or inclusion criteria, yet claims comprehensiveness (Section 1.3).

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Cite this review

Pith. "Pith review of Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures." pith.science (2026). https://pith.science/paper/CQRQMMLD

@misc{pith2026250206963,
  author       = {Pith},
  title        = {Pith review of: Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CQRQMMLD}},
  note         = {Machine review of arXiv:2502.06963}
}
read the original abstract

The increasing complexity of Intelligent Transportation Systems (ITS) has led to significant interest in computational offloading to external infrastructures such as edge servers, vehicular nodes, and UAVs. These dynamic and heterogeneous environments pose challenges for traditional offloading strategies, prompting the exploration of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) as adaptive decision-making frameworks. This survey presents a comprehensive review of recent advances in DRL-based offloading for vehicular edge computing (VEC). We classify and compare existing works based on learning paradigms (e.g., single-agent, multi-agent), system architectures (e.g., centralized, distributed, hierarchical), and optimization objectives (e.g., latency, energy, fairness). Furthermore, we analyze how Markov Decision Process (MDP) formulations are applied and highlight emerging trends in reward design, coordination mechanisms, and scalability. Finally, we identify open challenges and outline future research directions to guide the development of robust and intelligent offloading strategies for next-generation ITS.

Figures

Figures reproduced from arXiv: 2502.06963 by the authors.

Figure 1
Figure 1. Structure of this survey: DRL-based offloading strategies across centralized, distributed, and hierarchical edge [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Agent-environment interaction loop in RL. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Typical Central Offloading with single-agent system. [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Typical Distributed Offloading with single-agent sys [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 7
Figure 7. Figure 7: Typical Hierarchial Offloading with single-agent sys [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]

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Reference graph

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Pith tools

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