Latent Semantic State Estimation for Reliable Swarming of UAVs under Intermittent Connectivity

Paris A. Karakasis; Walid Saad

REVIEW 4 major objections 5 minor 29 references

Latent Semantic State Estimation for Reliable Swarming of UAVs under Intermittent Connectivity

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A memory-augmented predictor lets UAVs coordinate almost as well with dropped links as with full communication.

desk verdict A plausible MARL architecture for imputing missing peer messages in UAV swarms, but the headline robustness claim is untested in the exact regime—sustained outages—that the memory predictor is designed for. read the letter →

arxiv 2608.08895 v1 pith:CPQBBMI5 submitted 2026-08-09 eess.SP

Paris A. Karakasis , Walid Saad This is my paper · ORCID

classification eess.SP

keywords UAVswarmscooperativereconnaissanceintermittentcommunicationlatentstateestimationmulti-agentreinforcementlearningmemory-augmentedcoordinationsemanticcoverage

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 tries to establish that a swarm of reconnaissance UAVs can keep coordinated exploration nearly as effective under intermittent air-to-air communication as when every link is available, by having each drone predict missing peer messages in a learned latent space instead of reconstructing raw observations. The authors build a decentralized architecture in which every drone maintains a map state, a task state, and a recurrent memory state; when no peer message arrives, a generative predictor conditioned on the memory produces a substitute aggregated message. Simulations with three drones over a 50x50 grid show 90.98% final coverage at 30% link failure, within 1.5% of the full-communication upper bound, and coverage stays above 90% even at 50% failure. Removing the predictor drops coverage to 70.30%, and removing the memory drops it to 81.90%, showing both components are load-bearing. If the claim holds, intermittent connectivity need not be treated as a severe penalty for cooperative mapping and exploration, but as a condition the swarm can learn to overcome.

What carries the argument

The central object is the structured latent state, decomposed into a map state, a task state, and a memory state, where each drone broadcasts a fixed-size message formed by concatenating its map and task states. Incoming peer messages are reduced by mean pooling, a parameter-free permutation-invariant aggregation, to a single summarized message. When all links fail, a generative predictor $P_\zeta$ maps the recurrent memory state plus the latest local observation and proprioceptive state to a predicted substitute message; the map component of that prediction is withheld from the map-state update, while the task and memory updates consume it. This decomposition is what makes the estimation task tractable and directly aligned with the cooperative objective.

What would settle it

Run the same experiment with a larger swarm (for example, ten drones) in a larger or more structured environment and measure the coverage gap to full communication at a 30% link failure rate; if the gap grows substantially or collisions rise with swarm size, the mean-pooled latent aggregate has not captured the per-peer information coordination requires. Alternatively, record the mean-squared error of predicted messages against the true received messages on episodes where the predictor is confidently wrong and check whether those errors coincide with coverage loss.

Watch

Extended reading notes

Core claim

The central discovery is that a compact, task-oriented latent message—specifically, the mean-pooled concatenation of each peer's map and task states—contains enough coordination information that it can be substituted during outages by a generative model conditioned on the drone's own accumulated memory, and that this latent-space substitution recovers almost all of the coordination benefit of full communication. The paper demonstrates this through an ablation study: replacing the predicted message with a zero vector (no predictor) is worse than no communication at all (70.30% versus 83.44% coverage), indicating that an all-zero placeholder actively misleads the swarm, while removing the memory that conditions the predictor costs 9.08% coverage. The framework also uses an asymmetric update rule: the map state is never updated with predicted map content, so hallucinated messages cannot corrupt the collective occupancy estimate, while task and memory states do use the predicted content.

Load-bearing premise

The method assumes that a single 512-dimensional mean-pooled latent message, predicted from a drone's own memory, carries enough coordination-relevant information that the swarm's coverage and collision behavior resembles the full-communication case; this is tested only with three drones on a 50x50 grid with 30 randomly placed occupied cells.

Editorial extensions

If this is right

If the claim is correct, a swarm can maintain coordinated exploration with link failure probabilities up to 0.5 at a coverage level within 1.5% of full connectivity, with no monotonic degradation as failures become more frequent.
Latent-space prediction removes the need to reconstruct raw observations or high-dimensional spatial maps; drones exchange and predict compact 512-dimensional latent messages instead of raw peer data.
The ablation results imply that a wrong placeholder (all zeros) actively harms coordination more than having no information at all, so any dropout-handling mechanism must avoid injecting misleading substitutes.
Because map states only update from real messages, the collective map estimate should remain uncorrupted by hallucinated content during outages, preserving the integrity of the reconnaissance output.
The framework's use of shared actor parameters and centralized training with decentralized execution means the approach can in principle scale to arbitrary swarm sizes, provided the mean-pooled aggregate retains the needed coordination information.

Reading between the lines

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

A natural stress test is scaling the swarm beyond three drones and the environment beyond a 50x50 grid; mean pooling may smooth away per-agent identity, so the predictor's sufficiency at larger scales is untested and may degrade if individual peer states matter for collision avoidance.
The predictor's conditioning on a recurrent memory suggests a testable extension where the generated message is accompanied by an uncertainty or confidence estimate, which could gate how strongly the task state trusts predicted content during long outages.
Because coverage stays flat across link failure probabilities, the bottleneck may shift from communication availability to the quality of the learned latent representation; benchmarking against explicit frontier-sharing on larger, structured maps would reveal when latent compression loses information.
The same memory-conditioned prediction idea could apply to other intermittent multi-agent coordination tasks, such as search-and-rescue or environmental monitoring, wherever a compact task-relevant summary can substitute for raw peer data.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

This is a clean MARL architecture for keeping a UAV reconnaissance swarm coordinated when A2A links drop. Each drone maintains a structured latent state (map, task, memory); when no peer message arrives, a memory-conditioned generative model imputes the missing aggregated message in latent space. The core idea is sensible, and the ablations show both the predictor and the memory carry real weight. But the central robustness claim—that the framework closely matches a fully connected swarm—is validated only under per-step i.i.d. link failures with N=3, and the training loss never directly supervises the predictor on consecutive dropout steps. That is a genuine hole, not a quibble.

What is actually new: the map/task/memory decomposition is a reasonable combination relative to prior work that imputes raw observations. The map-shielding trick—zeroing the map component of a missing message so the map update never ingests hallucinated content—is simple, sensible, and probably the cleanest contribution. The ablation table is informative: removing the predictor drops coverage from ~91% to ~70% and raises collisions sixfold, so the predictor is not decorative. The pf sweep from 0.009 to 0.50 shows stable coverage, which is mildly encouraging.

The load-bearing problem is the training objective. Eq. (4) sums Lpred only over T_comm, the steps with at least one active link. The predictor is therefore supervised one step ahead on real messages and is never directly trained with its own predictions fed back into memory across a multi-step outage. End-to-end reward gradients can partially compensate, but the primary regression signal does not cover the regime the method is marketed for. The paper's own No comm lower bound—83.44% versus 92.40% for full comm—shows that sustained loss of all links is exactly where the margin collapses. The stress-test note is right: correlated or bursty dropouts, which real A2A channels exhibit and Eq. (1) does not model, are untested. With N=3 and p_f=0.5, the expected duration of a complete outage is only four steps, so the memory rarely has to bridge a long gap.

Also missing: any comparison to prior methods (explicitly deferred to future work) and no code or data release. The environment is tiny—three drones on a 50x50 grid with 30 random occupied cells—which is fine as a proof of concept but not yet a demonstration of reliability. The observation model P_o is never specified beyond "noisy," which makes the map reconstruction loss hard to interpret. These are standard weaknesses for a proof-of-concept MARL paper, not fatal flaws. The self-citation is just a survey and is not load-bearing.

Who is this for? People working on semantic or task-oriented communication, or MARL under intermittent connectivity. It is not a definitive validation, but it is a reasonable architectural proposal with an honest ablation section. I would send it to peer review—a good referee would require sustained-outage tests and external baselines, and the paper would be better for it. I would not cite it yet, and I'd skip it for a reading group unless someone wants to discuss training-objective gaps in MARL.

Referee Report

4 major / 5 minor

Summary. The paper proposes a memory-augmented framework for multi-UAV reconnaissance under intermittent air-to-air communication. Each UAV maintains a structured latent state with map, task, and memory components; when all peer links are lost, a generative predictor infers a substitute aggregated peer message from the UAV's memory state. The framework is trained end-to-end using MAPPO under the CTDE paradigm, with an asymmetric update that prevents hallucinated map updates during dropout. Simulations on a small grid (N=3, 50×50, 30 occupied cells) report that the proposed method achieves 90.98±2.92% final coverage at a link failure probability of 0.3, within 1.5% of a fully connected swarm's 92.40%, and that coverage stays between 90.89% and 92.59% for failure probabilities from 0 to 0.5. Ablations show that removing the predictor reduces coverage to 70.30% and removing the memory reduces it to 81.90%, supporting the contribution of both components.

Significance. If the approach holds, it offers a compact, task-oriented latent representation for sustaining swarm coordination during communication outages, which is a relevant problem in UAV networks and semantic communication. The paper's strengths include a clean architectural decomposition, well-designed ablations that isolate the predictor and memory contributions, and a robustness sweep over link failure probabilities. However, the evaluation is limited to a small synthetic scenario with no comparison to prior methods, and the central numerical claim is based on 10 Monte Carlo runs with overlapping standard deviations. The significance is therefore moderate and currently rests on the internal consistency of the experiments rather than on demonstrated advantages over existing approaches.

major comments (4)

[III-E, Eq. (4)] The prediction loss L_pred is summed only over T_comm = {t : N_i_t != empty}, i.e., only over time steps where at least one real message is received. The predictor is therefore never directly trained on its own predicted messages being fed back into the task and memory states, which is exactly the regime that occurs during sustained outages. The No comm condition in Table I (83.44% coverage vs. 92.40% for full communication) shows that when the predictor must operate continuously, the gap is about 9 percentage points, not the claimed 1.5%. This undermines the generality of the central claim and indicates that the method is validated only for short, isolated dropouts. The authors should train with predicted feedback (e.g., scheduled sampling or a multi-step prediction objective) or explicitly evaluate on correlated/bursty outage episodes and qualify the claim accordingly.
[Section IV] The evaluation lacks any comparison to existing methods. The related work in Section I-A criticizes prior approaches for operating on raw observations or treating dropouts as noise, but no prior-art baseline is implemented or tested. The conclusion explicitly defers benchmarking against explicit-communication mapping approaches [18], [19] to future work. Without at least one comparative baseline (e.g., message-dropout [9] or a raw-observation exchange scheme), the paper cannot substantiate its positioning as an improvement over the state of the art. The internal ablations are informative but do not address this gap.
[Section II, observation model; Section III-C] The observation model P_o is defined in the Dec-POMDP but never specified. The simulation mentions a sensor range of 2 cells, yet the content, dimension, noise model, and partial-observability mechanism of the observation vector o_i_t are not described. Similarly, the memory-based predictor P_zeta is described only as a generative model; its architecture, output parameterization, and sampling procedure are not given. These omissions make the experiments non-reproducible and leave it unclear whether the method relies on unrealistically informative observations or a particular predictor form.
[Section IV, Table I and robustness sweep] The central numerical claim of 'within 1.5%' is based on a difference of 1.42 percentage points between 90.98±2.92% and 92.40±2.64% over 10 runs. The standard deviations are larger than the observed difference, and no statistical test, confidence interval, or per-run paired comparison is reported. Similarly, the collision counts (8.70±8.49 vs. 10.30±12.54) have very high relative variance, so the qualitative conclusions about matching the fully connected swarm are not statistically anchored. The paper should report paired differences across runs or otherwise quantify the uncertainty of the claimed margin.

minor comments (5)

[Title and throughout] The term 'UA Vs' is used instead of the standard 'UAVs' in the title and body, which is typographically awkward and should be normalized.
[Author affiliation] The affiliation line reads 'Institute for Advanced ComputingandBradley Dept.'; a space is missing between 'Computing' and 'and'.
[Section III-A] The dimensions d_mu, d_tau, and d_rho are introduced without specifying their values in the main text; the values appear only in Section IV. Please state them when the latent state is first defined.
[Section IV, robustness sweep] In the right half of Table I, the 'Steps to 90%' column reports single numbers without standard deviations, while the left half reports mean±std. Please either add uncertainty measures or note that these are point estimates from the 10 runs.
[Section II-A] Equation (1) includes the Bernoulli indicator ξ, but the probability law of ξ and its relation to p_f are not given until Section IV. Please define p_f and the link activation rule explicitly near the channel model.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central coverage claim is evaluated against externally measured observations, not against the predictor's own outputs.

full rationale

The paper's central claim is empirical: the memory-augmented framework reaches 90.98±2.92% coverage under p_f=0.3, within 1.5% of a fully connected swarm. Coverage in Table I is measured from the set C(M_hat_t) of cells actually observed by any UAV, not from the latent substitute messages produced by the predictor, so the headline number is not definitionally tied to the learned imputation. The predictor is trained with an auxiliary self-supervised loss Lpred in Eq. (4), which minimizes MSE between received aggregated messages and predicted substitutes; this is a standard imputation training signal, not a fitted parameter that is later renamed as a prediction. The map state is regularized by Lmap to reconstruct the global map estimate, but the reported coverage is computed from real observations and the policy is trained with MAPPO on a shared reward; no load-bearing step reduces to its own input. The only self-citation, ref. [6] (Mozaffari, Saad, Bennis, Nam, Debbah), is used as background for UAV channel factors and does not supply any premise of the proposed method. The robustness sweep uses i.i.d. per-step Bernoulli link failures and does not test sustained correlated outages, and Lpred is only supervised on steps with at least one active neighbor; these are evaluation limitations that bear on external validity and correctness risk, not on circularity. No equation or construction in the paper is equivalent by definition to a claimed output, so no circular step can be exhibited.

Assumptions & free parameters 7 free parameters · 5 assumptions · 2 invented entities

The central claim depends on stationarity of the training distribution and on the latent representation being sufficient for coordination. Several hand-chosen hyperparameters and the small simulated environment are the main free parameters.

free parameters (7)

Latent dimensions (d_mu, d_tau, d_rho) = 512 for all
Chosen by hand; capacity of the compressed representations affects how much coordination information can be retained and predicted.
Reward weights = w_cov=0.4, w_red=0.02, w_coll=0.8
Manual trade-offs between exploration coverage, redundancy penalty, and collision avoidance shape the learned policy.
Loss weights = c1=0.5, c2=c3=5
Balance MAPPO policy gradient against map reconstruction and message prediction self-supervised losses.
Sensor range = 2 cells
Determines how much of the environment each drone can observe locally; changing it changes the difficulty of coordination.
Swarm size = N=3
Validation is limited to three drones; the method's scaling to larger swarms is untested.
Training link failure probability = p_f=0.3
The training distribution of dropouts; the paper sweeps test-time p_f but training uses a single value.
Rician channel parameters = K=5, L_atm=0.9, alpha=2, P_j=20dBm, beta_th=0.05, sigma^2=1
Simulated A2A channel model parameters chosen to pose a meaningful challenge; no real-world calibration.

assumptions (5)

domain assumption The Dec-POMDP with shared reward is an appropriate model for cooperative UAV reconnaissance.
The paper formalizes the problem as a Dec-POMDP and trains with CTDE; if cooperation cannot be captured by a shared scalar reward, the optimization target would be misspecified.
domain assumption The Rician fading model in Sec. II-A approximates real air-to-air UAV channels well enough for the simulation conclusions to transfer.
The link failure process is generated by this model; the robustness claims are only about this model's distribution.
domain assumption Mean-pooling of peer latent messages preserves coordination-relevant information.
Sec. III-B replaces the set of peer messages with their mean; if critical information is lost in averaging, the whole framework would underperform in heterogeneous swarms.
domain assumption The memory state accumulates sufficient history for the generative predictor to recover the aggregated message.
Sec. III-C and the 'No memory' ablation show the predictor depends on this state, but its sufficiency in more complex missions is not proven.
standard math The training objective L in Eq. (2) and MAPPO converge to a policy that generalizes to the simulated test conditions.
Relies on standard RL convergence assumptions and on the self-supervised losses guiding the latent states.

invented entities (2)

Structured latent state (map, task, memory)
purpose: Provides a compact representation of the swarm state and history for coordination and prediction.
Only evaluated within the paper's simulator; no external data or real-world validation.
Memory-based generative predictor P_zeta
purpose: Generates substitute aggregated messages during communication dropouts.
Its accuracy is measured only through the L_pred training loss and downstream simulation performance.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Latent Semantic State Estimation for Reliable Swarming of UAVs under Intermittent Connectivity." pith.science (2026). https://pith.science/paper/CPQBBMI5

@misc{pith2026260808895,
  author       = {Pith},
  title        = {Pith review of: Latent Semantic State Estimation for Reliable Swarming of UAVs under Intermittent Connectivity},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CPQBBMI5}},
  note         = {Machine review of arXiv:2608.08895}
}

read the original abstract

Cooperative multi-unmanned aerial vehicle (UAV) reconnaissance is often hindered by intermittent air-to-air communications where link dropouts lead to uncoordinated exploration and redundant mapping. Existing approaches rely on explicit exchange of high-dimensional spatial data or raw observations, incurring significant overhead, and often revert to reactive individual exploration during outages. This paper proposes a memory-augmented framework in which each UAV maintains a structured latent state decomposed into map, task, and memory components. During dropout, a generative predictor conditioned on the memory state infers substitute peer messages in the latent space, making the estimation task more tractable and directly aligned with the cooperative objective. The framework is trained end-to-end under the centralized training with decentralized execution paradigm. Simulation results demonstrate that the proposed framework closely matches the performance of a fully connected swarm, while remaining robust across a wide range of link failure conditions.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

29 extracted references · 25 canonical work pages

[18]

Communicating multi-UA V system for cooperative SLAM-based exploration,

N. Mahdoui, V . Fr ´emont, and E. Natalizio, “Communicating multi-UA V system for cooperative SLAM-based exploration,”J. Intell. Robot. Syst., vol. 98, no. 2, pp. 325–343, 2020

work page 2020
[19]

Multi-UA V adaptive path planning using deep reinforcement learning,

J. Westheider, J. R ¨uckin, and M. Popovi ´c, “Multi-UA V adaptive path planning using deep reinforcement learning,” inProc. of IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2023

work page 2023
[9]

Message-dropout: An efficient training method for multi-agent deep reinforcement learning,

W. Kim, M. Cho, and Y . Sung, “Message-dropout: An efficient training method for multi-agent deep reinforcement learning,” inProc. of AAAI Conf. Artificial Intelligence, 2019

work page 2019
[1]

A Survey of Unmanned Aerial Vehicles (UA V) for Traffic Surveillance,

A. Puri, “A Survey of Unmanned Aerial Vehicles (UA V) for Traffic Surveillance,” Technical Report, Department of Computer Science and Engineering, University of South Florida, 2005, pp. 1–29

work page 2005
[2]

UA V swarm commu- nication and control architectures: A review,

M. Campion, P. Ranganathan, and S. Faruque, “UA V swarm commu- nication and control architectures: A review,”J. Unmanned Veh. Syst., vol. 7, no. 2, pp. 93–106, 2019

work page 2019
[3]

A comprehensive survey on UA V communication channel modeling,

C. Yan, L. Fu, J. Zhang, and J. Wang, “A comprehensive survey on UA V communication channel modeling,”IEEE Access, vol. 7, pp. 107769– 107792, 2019

work page 2019
[4]

Investigation of air-to-air channel characteristics and a UA V specific extension to the Rice model,

N. Goddemeier and C. Wietfeld, “Investigation of air-to-air channel characteristics and a UA V specific extension to the Rice model,” inProc. of IEEE Globecom Workshops, 2015

work page 2015
[5]

Survey of important issues in UA V communication networks,

L. Gupta, R. Jain, and G. Vaszkun, “Survey of important issues in UA V communication networks,”IEEE Commun. Surveys Tuts., vol. 18, no. 2, pp. 1123–1152, 2016

work page 2016

Show all 29 references

[6]

A tutorial on UA Vs for wireless networks: Applications, challenges, and open problems,

M. Mozaffari, W. Saad, M. Bennis, Y .-H. Nam, and M. Debbah, “A tutorial on UA Vs for wireless networks: Applications, challenges, and open problems,”IEEE Commun. Surveys Tuts., vol. 21, no. 3, pp. 2334– 2360, 2019

2019
[7]

UA V channel models: Review and future research,

D. W. Matolak and U.-C. Fiebig, “UA V channel models: Review and future research,” inProc. of 13th European Conference on Antennas and Propagation (EuCAP), 2019

2019
[8]

Multiagent cooperative search learning with intermittent communication,

R. Zhang, J. Wang, J. Ge, and Q. Huang, “Multiagent cooperative search learning with intermittent communication,”IEEE Intell. Syst., vol. 39, no. 2, pp. 11–20, Mar./Apr. 2024

2024
[10]

Semantic communications: Principles and challenges,

Z. Qin, X. Tao, J. Lu, and G. Y . Li, “Semantic communications: Principles and challenges,”arXiv preprint arXiv:2212.00556, 2022

2022 arXiv
[11]

Deep learning enabled semantic communication systems,

H. Xie, Z. Qin, G. Y . Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,”IEEE Trans. Signal Process., vol. 69, pp. 2663–2675, 2021

2021
[12]

Learning multiagent commu- nication with backpropagation,

S. Sukhbaatar, A. Szlam, and R. Fergus, “Learning multiagent commu- nication with backpropagation,” inProc. of Adv. in Neural Inf. Process Syst. (NeurIPS), 2016

2016
[13]

Learning to communicate with deep multi-agent reinforcement learning,

J. Foerster, I. A. Assael, N. de Freitas, and S. Whiteson, “Learning to communicate with deep multi-agent reinforcement learning,” inProc. of Adv. in Neural Inf. Process Syst. (NeurIPS), 2016

2016
[14]

Multiple unmanned aerial vehicle (multi-UA V) reconnaissance and search with limited communication range using semantic episodic memory in rein- forcement learning,

B. Zhang, T. Wang, M. Li, Y . Cui, X. Lin, and Z. Zhu, “Multiple unmanned aerial vehicle (multi-UA V) reconnaissance and search with limited communication range using semantic episodic memory in rein- forcement learning,”Drones, vol. 8, no. 8, p. 393, 2024

2024
[15]

Multi-agent reinforcement learning for cooperative search under aperiodically intermittent communication,

L. Fu, J. Wang, and H. Luo, “Multi-agent reinforcement learning for cooperative search under aperiodically intermittent communication,” Expert Syst. Appl., vol. 280, p. 127526, 2025

2025
[16]

FCMNet: Full communication multi-hop network for multi-agent reinforcement learning,

N. Guan, T. Duong, and G. S. Soh, “FCMNet: Full communication multi-hop network for multi-agent reinforcement learning,” inProc. of ACM Int. Conf. Auton. Agents and Multiagent Syst. (AAMAS), 2022

2022
[17]

Centralized training with hybrid execution in multi- agent reinforcement learning via predictive observation imputation,

P. P. Santos et al., “Centralized training with hybrid execution in multi- agent reinforcement learning via predictive observation imputation,” Artificial Intelligence, vol. 348, p. 104404, 2025

2025
[20]

Specific attenuation model for rain for use in prediction methods,

Int. Telecommunication Union, “Specific attenuation model for rain for use in prediction methods,”ITU-R Recommendation P .838-3, 2005

2005
[21]

An introduction to centralized training for decentralized execution in cooperative multi-agent reinforcement learning,

C. Amato, “An introduction to centralized training for decentralized execution in cooperative multi-agent reinforcement learning,”arXiv preprint arXiv:2409.03052, 2024

2024 arXiv
[22]

The surprising effectiveness of PPO in cooperative multi-agent games,

C. Yu, A. Velu, E. Vinitsky, J. Gao, P. Abbeel, and S. Ermon, “The surprising effectiveness of PPO in cooperative multi-agent games,” in Proc. of Adv. in Neural Inf. Process Syst. (NeurIPS), 2022

2022
[23]

Learning phrase representations using RNN encoder-decoder for statistical machine translation,

K. Cho, B. van Merri ¨enboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y . Bengio, “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” inProc. of Conf. Empirical Methods Natural Language Processing (EMNLP), 2014

2014
[24]

Multi- agent actor-critic for mixed cooperative-competitive environments,

R. Lowe, Y . Wu, A. Tamar, J. Harb, P. Abbeel, and I. Mordatch, “Multi- agent actor-critic for mixed cooperative-competitive environments,” in Proc. of Adv. in Neural Inf. Process Syst. (NeurIPS), 2017

2017
[25]

R. S. Sutton and A. G. Barto,Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA: MIT Press, 2018

2018
[26]

Actor-critic algorithms,

V . R. Konda and J. N. Tsitsiklis, “Actor-critic algorithms,” inProc. of Adv. in Neural Inf. Process Syst. (NeurIPS), 2000

2000
[27]

Efficient resource allocation for multi-UA V communication against adjacent and co-channel interfer- ence,

L. Zhou, X. Chen, M. Hong, S. Jinet al., “Efficient resource allocation for multi-UA V communication against adjacent and co-channel interfer- ence,”IEEE Trans. Veh. Technol., 2021

2021
[28]

Optimum deployment of multiple UA Vs for coverage area maximization in the presence of co- channel interference,

A. A. Khuwaja, G. Zheng, Y . Chen, and W. Feng, “Optimum deployment of multiple UA Vs for coverage area maximization in the presence of co- channel interference,”IEEE Access, 2019

2019
[29]

A survey of path loss prediction and channel models for unmanned aerial systems for system-level simulations,

N. Moraitis, K. Psychogios, and A. D. Panagopoulos, “A survey of path loss prediction and channel models for unmanned aerial systems for system-level simulations,”Sensors, vol. 23, no. 10, p. 4775, 2023

2023

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [18]

Communicating multi-UA V system for cooperative SLAM-based exploration,

N. Mahdoui, V . Fr ´emont, and E. Natalizio, “Communicating multi-UA V system for cooperative SLAM-based exploration,”J. Intell. Robot. Syst., vol. 98, no. 2, pp. 325–343, 2020

work page 2020

[2] [19]

Multi-UA V adaptive path planning using deep reinforcement learning,

J. Westheider, J. R ¨uckin, and M. Popovi ´c, “Multi-UA V adaptive path planning using deep reinforcement learning,” inProc. of IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2023

work page 2023

[3] [9]

Message-dropout: An efficient training method for multi-agent deep reinforcement learning,

W. Kim, M. Cho, and Y . Sung, “Message-dropout: An efficient training method for multi-agent deep reinforcement learning,” inProc. of AAAI Conf. Artificial Intelligence, 2019

work page 2019

[4] [1]

A Survey of Unmanned Aerial Vehicles (UA V) for Traffic Surveillance,

A. Puri, “A Survey of Unmanned Aerial Vehicles (UA V) for Traffic Surveillance,” Technical Report, Department of Computer Science and Engineering, University of South Florida, 2005, pp. 1–29

work page 2005

[5] [2]

UA V swarm commu- nication and control architectures: A review,

M. Campion, P. Ranganathan, and S. Faruque, “UA V swarm commu- nication and control architectures: A review,”J. Unmanned Veh. Syst., vol. 7, no. 2, pp. 93–106, 2019

work page 2019

[6] [3]

A comprehensive survey on UA V communication channel modeling,

C. Yan, L. Fu, J. Zhang, and J. Wang, “A comprehensive survey on UA V communication channel modeling,”IEEE Access, vol. 7, pp. 107769– 107792, 2019

work page 2019

[7] [4]

Investigation of air-to-air channel characteristics and a UA V specific extension to the Rice model,

N. Goddemeier and C. Wietfeld, “Investigation of air-to-air channel characteristics and a UA V specific extension to the Rice model,” inProc. of IEEE Globecom Workshops, 2015

work page 2015

[8] [5]

Survey of important issues in UA V communication networks,

L. Gupta, R. Jain, and G. Vaszkun, “Survey of important issues in UA V communication networks,”IEEE Commun. Surveys Tuts., vol. 18, no. 2, pp. 1123–1152, 2016

work page 2016