pith. sign in

arxiv: 2605.17999 · v1 · pith:KMS4ON4Cnew · submitted 2026-05-18 · 💻 cs.AI

Shared Backbone PPO for Multi-UAV Communication Coverage with Connection Preservation

Z. Jiang This is my paper

Pith reviewed 2026-05-20 11:24 UTC · model grok-4.3

classification 💻 cs.AI
keywords Shared Backbone PPOMulti-UAV communicationConnection preservationProximal Policy OptimizationGraph information aggregationMulti-agent reinforcement learningSwarm coverage
0
0 comments X

The pith

Shared Backbone PPO improves multi-UAV swarm coverage by sharing the base module between actor and critic networks.

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

This paper proposes a Shared Backbone Proximal Policy Optimization algorithm for connectivity-preserving multi-UAV swarm communication coverage tasks. By sharing the base module between the Actor and Critic networks, the method enables more efficient training and achieves better results than standard PPO. The approach further adds a graph information aggregation module to handle communication among agents, producing higher levels of cooperation in the swarm. A sympathetic reader would care because the work shows how a modest architectural change in reinforcement learning can support practical multi-agent coordination under connection constraints.

Core claim

The Shared Backbone PPO algorithm, by sharing the base module between Actor and Critic networks, achieves efficient training and superior performance in the connectivity-preserving multi-UAV swarm communication coverage task compared with the standard PPO algorithm. With the addition of a graph information aggregation module to accommodate communication conditions among agents, the algorithm remains effective and the trained agent swarm exhibits a higher level of cooperation.

What carries the argument

The shared base module between the Actor and Critic networks, which carries the argument by enabling parameter sharing for more efficient and stable learning in the multi-agent UAV setting.

If this is right

  • The method achieves superior performance compared with standard PPO in the connectivity-preserving multi-UAV swarm communication coverage task.
  • The trained agent swarm exhibits a higher level of cooperation.
  • The algorithm remains effective after the graph information aggregation module is incorporated into the model architecture.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The sharing technique may apply to other multi-agent reinforcement learning problems that involve communication constraints and coverage objectives.
  • Parameter sharing could reduce overall training compute in swarm robotics tasks while maintaining connection preservation.
  • Physical drone experiments would test whether the observed cooperation gains translate to real-world radio environments.

Load-bearing premise

Sharing the base module between Actor and Critic networks will produce stable and improved learning without introducing interference or requiring extensive retuning in the UAV coverage environment.

What would settle it

A side-by-side run of Shared Backbone PPO and standard PPO on the identical connectivity-preserving multi-UAV communication coverage task in which the shared version shows equal or worse performance metrics.

Figures

Figures reproduced from arXiv: 2605.17999 by Z. Jiang.

Figure 1
Figure 1. Figure 1: UAV Swarm Networks for Communication Coverage [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: PPO structual 2-1 [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: Reward Curves without Graph Aggrega￾tor [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Coverage Curves without Graph Aggre￾gator [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Energy Curves without Graph Aggrega￾tor [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Coverage before Train [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
read the original abstract

This paper proposes a Shared Backbone Proximal Policy Optimization (Shared Backbone PPO) algorithm. By sharing the base module between the Actor and Critic networks, the algorithm achieves efficient training and improved performance. The algorithm is implemented in a connectivity-preserving multi-UAV swarm communication coverage task and compared with the standard PPO algorithm. Experimental results demonstrate that the proposed method achieves superior performance. Furthermore, a graph information aggregation module is incorporated into the model architecture to accommodate the communication conditions among agents. With the integration of this module, the algorithm remains effective, and the trained agent swarm exhibits a higher level of cooperation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper proposes a Shared Backbone Proximal Policy Optimization (Shared Backbone PPO) algorithm for a connectivity-preserving multi-UAV swarm communication coverage task. By sharing the base module between Actor and Critic networks and adding a graph information aggregation module to handle agent communication, the method is claimed to enable efficient training, superior performance over standard PPO, and higher cooperation levels in the agent swarm.

Significance. If the performance gains can be isolated to the shared backbone and hold under controlled comparisons, the approach could provide a lightweight architectural improvement for multi-agent RL in UAV coverage problems, aiding stable learning and connectivity preservation. The incorporation of graph aggregation for communication is a relevant adaptation, but its interaction with the backbone sharing requires clearer separation to establish the contribution.

major comments (1)
  1. Abstract: The central claim attributes superior performance to the Shared Backbone PPO (defined by sharing the base module between Actor and Critic). However, the abstract states that a graph information aggregation module is incorporated 'to accommodate the communication conditions among agents' and that 'with the integration of this module, the algorithm remains effective.' It is not specified whether the standard PPO baseline includes this identical graph module. If the baseline omits it, gains in coverage or connectivity metrics could stem from the graph module rather than the backbone sharing, leaving the specific contribution of the proposed algorithm unisolated.
minor comments (1)
  1. Abstract: No quantitative results, baselines, error bars, or training curves are reported despite the claim of superior performance; these details should appear in the experiments section to support the empirical comparison.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment point by point below and agree that greater clarity is needed to isolate contributions.

read point-by-point responses

  1. Referee: Abstract: The central claim attributes superior performance to the Shared Backbone PPO (defined by sharing the base module between Actor and Critic). However, the abstract states that a graph information aggregation module is incorporated 'to accommodate the communication conditions among agents' and that 'with the integration of this module, the algorithm remains effective.' It is not specified whether the standard PPO baseline includes this identical graph module. If the baseline omits it, gains in coverage or connectivity metrics could stem from the graph module rather than the backbone sharing, leaving the specific contribution of the proposed algorithm unisolated.

    Authors: We thank the referee for identifying this ambiguity. The graph information aggregation module is an adaptation incorporated into our Shared Backbone PPO architecture specifically to handle inter-agent communication conditions required by the connectivity-preserving multi-UAV coverage task. The standard PPO baseline follows the vanilla implementation without either the shared backbone or the graph module. To isolate the contribution of the shared backbone more clearly, we will revise the abstract to explicitly distinguish the components of the proposed method from the baseline. We will also expand the experimental setup description to detail how baselines are configured. These changes will allow readers to attribute performance differences more precisely to the backbone sharing while retaining the graph module as a task-specific necessity. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical performance claims rest on experimental comparison

full rationale

The paper proposes Shared Backbone PPO by sharing the base module between Actor and Critic, incorporates a graph information aggregation module, and reports superior empirical performance versus standard PPO in the multi-UAV connectivity-preserving coverage task. No equations, derivations, or self-referential predictions appear in the abstract or described content. The central claim is an empirical outcome rather than a quantity derived by construction from fitted parameters or prior self-citations. The graph module is presented as an architectural addition that preserves effectiveness, but this does not reduce any derivation to its own inputs. No load-bearing self-citation chains or uniqueness theorems are invoked. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No explicit free parameters, axioms, or invented entities are stated in the abstract. The method implicitly assumes standard PPO stability properties and that the graph module adds useful communication information without further justification.

pith-pipeline@v0.9.0 · 5611 in / 1088 out tokens · 28587 ms · 2026-05-20T11:24:19.135136+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages · 4 internal anchors

  1. [1]

    DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

    Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J., Bi, X., Zhang, H., Zhang, M., Li, Y. K., Wu, Y., et al. (2024). Deepseekmath: Pushing the limits of mathematical reasoning in open language models.arXiv preprint arXiv:2402.03300

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  2. [2]

    M., & Moh, S

    Alam, M. M., & Moh, S. (2022). Survey on Q-learning-based position-aware routing protocols in flying ad hoc networks.Electronics, 11(7), 1099

    work page 2022

  3. [3]

    Wu, S., Pu, Z., Qiu, T., Yi, J., & Zhang, T. (2022). Deep-reinforcement-learning-based mul- titarget coverage with connectivity guaranteed.IEEE Transactions on Industrial Informatics, 19(1), 121–132

    work page 2022

  4. [4]

    Rezwan, S., & Choi, W. (2021). A survey on applications of reinforcement learning in flying ad-hoc networks.Electronics, 10(4), 449

    work page 2021

  5. [5]

    Pasandideh, F., da Costa, J. P. J., Kunst, R., Islam, N., Hardjawana, W., & Pignaton de Freitas, E. (2022). A review of flying ad hoc networks: Key characteristics, applications, and wireless technologies.Remote Sensing, 14(18), 4459

    work page 2022

  6. [6]

    Jiang, Z., Chen, Y., Wang, K., Yang, B., & Song, G. (2023). A graph-based PPO approach in multi-UAV navigation for communication coverage.International Journal of Computers Com- munications & Control, 18(6)

    work page 2023

  7. [7]

    Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms.arXiv preprint arXiv:1707.06347

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  8. [8]

    Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. InInternational Conference on Machine Learning, pp. 1861–1870. PMLR

    work page 2018

  9. [9]

    Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning.Machine Learning, 8, 279–292

    work page 1992

  10. [10]

    Mnih, V. (2013). Playing Atari with deep reinforcement learning.arXiv preprint arXiv:1312.5602

    work page internal anchor Pith review Pith/arXiv arXiv 2013

  11. [11]

    Jiang, Z., Song, T., Yang, B., & Song, G. (2024). Fault-tolerant control for multi-UAV explo- ration system via reinforcement learning algorithm.Aerospace, 11(5), 372

    work page 2024

  12. [12]

    Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al. (2025). Deepseek-r1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501.12948

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  13. [13]

    Jiang, Z., Chen, Y., Song, G., Yang, B., & Jiang, X. (2023). Cooperative planning of multi- UAV logistics delivery by multi-graph reinforcement learning. InInternational Conference on Computer Application and Information Security (ICCAIS), pp. 129–137. SPIE

    work page 2023

  14. [14]

    Han, Y., Zhang, L., & Meng, D. (2025). A differentiated reward method for reinforce- ment learning based multi-vehicle cooperative decision-making algorithms.arXiv preprint arXiv:2502.00352

    work page arXiv 2025

  15. [15]

    Ray, A., Achiam, J., & Amodei, D. (2019). Benchmarking safe exploration in deep reinforcement learning. Available at: https://cdn.openai.com/safexp-short.pdf 13

    work page 2019

  16. [16]

    Zhang, Z., Zhang, Q., Wu, X., Shi, X., Liao, G., Wang, Y., Wang, X., & Zhao, D. (2024). User response modeling in reinforcement learning for ads allocation. InCompanion Proceedings of the ACM Web Conference, pp. 131–140

    work page 2024

  17. [17]

    Bai, Y., Zhao, H., Zhang, X., Chang, Z., J¨ antti, R., & Yang, K. (2023). Towards autonomous multi-UAV wireless network: A survey of reinforcement learning-based approaches.IEEE Com- munications Surveys & Tutorials

    work page 2023

  18. [18]

    S., Yousefpoor, M

    Hosseinzadeh, M., Ali, S., Ionescu-Feleaga, L., Ionescu, B. S., Yousefpoor, M. S., Yousefpoor, E., Ahmed, O. H., Rahmani, A. M., & Mehmood, A. (2023). A novel Q-learning-based routing scheme using an intelligent filtering algorithm for flying ad hoc networks (FANETs).Journal of King Saud University - Computer and Information Sciences, 35(10), 101817

    work page 2023

  19. [19]

    Y., & Moh, S

    Arafat, M. Y., & Moh, S. (2021). A Q-learning-based topology-aware routing protocol for flying ad hoc networks.IEEE Internet of Things Journal, 9(3), 1985–2000

    work page 2021

  20. [20]

    Sutton, R. S. (2018).Reinforcement learning: An introduction. A Bradford Book

    work page 2018

  21. [21]

    Cao, L., Yue, Y., Cai, Y., & Zhang, Y. (2021). A novel coverage optimization strategy for heterogeneous wireless sensor networks based on connectivity and reliability.IEEE Access, 9, 18424–18442

    work page 2021

  22. [22]

    Trotta, A., Montecchiari, L., Di Felice, M., & Bononi, L. (2020). A GPS-free flocking model for aerial mesh deployments in disaster-recovery scenarios.IEEE Access, 8, 91558–91573. 14

    work page 2020