A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO

Billy Pik Lik Lau; Chau Yuen; Leon Fernando; U-Xuan Tan

REVIEW 4 major objections 6 minor 16 references

A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A recurrent PPO policy lets a single drone find a signal-emitting target in a GPS-denied indoor grid with 93% success, while a second drone shortens the search.

desk verdict A clean but thin simulation study of Recurrent PPO for UAV target search; the headline accuracy is just entry into a coarse signal region, and the paper never says how big that region is. read the letter →

arxiv 2412.06231 v1 pith:ETACMNQD submitted 2024-12-09 cs.RO cs.LG

Leon Fernando , Billy Pik Lik Lau , Chau Yuen , U-Xuan Tan This is my paper · ORCID

classification cs.ROcs.LG

keywords targetlocalizationUAVswarmrecurrentPPOLSTMpartialobservabilityGPS-deniednavigationgridexplorationdecentralizedreinforcementlearning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to show that a reinforcement-learning agent with recurrent memory can find a signal-emitting target indoors without GPS, using only a small grid map and a three-level signal-strength reading. The single-drone policy reaches the target in 93% of 100 test scenarios, averaging 183.82 steps. A decentralized two-drone variant, sharing one grid map, reaches 86% success while cutting the best average to 162.89 steps. If these results hold up, the approach offers a low-hardware route to search-and-rescue and monitoring in GPS-denied, visually degraded spaces, and a starting point for scaling to larger UAV swarms.

What carries the argument

The machinery is a Recurrent PPO policy with an LSTM layer of 256 hidden units on top of a PPO base, trained on a custom grid simulator. The observation is 17 inputs for a single drone (8 obstacle flags, 8 map-state readings, 1 signal level) and 21 for two drones (plus 4 quadrant flags for nearby drones); the action is one of eight translations. The reward system shapes behavior with +2 for exploring unknown cells, penalties for obstacles and repeat visits, a signal-strength cost up to +250, +1000 on reaching the target, and -2/-50 for proximity or collision with another drone. The map states (unknown, obstacle, traveled once/twice/thrice) are shared across drones in the multi-agent setting.

What would settle it

Deploy the trained policies unchanged on physical quadrotors in an indoor testbed with an RF beacon, walls, and clutter; if the measured success rate falls far below the simulator's 93% and 86% (for example below 50%) or the drones fail because of multipath, dropped signals, or delays, then the simulation is not a faithful proxy and the central accuracy claim does not transfer.

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Extended reading notes

Core claim

The paper's central claim is that a Recurrent PPO model—a Proximal Policy Optimization policy augmented with an LSTM layer—can solve target localization in perceptually degraded grid environments from sparse observations. In a simulator with eight surrounding obstacle flags, eight surrounding map states, and a coarse signal strength, the single-drone model reaches 93% success across 100 scenario configurations built from 10 indoor-layout maps. The two-drone model, which adds four nearby-drone presence indicators and shares a common explored grid map, retains 86% success and reduces the average number of steps to 162.89 at its 86% checkpoint (the 83% checkpoint averages 130.08). The paper interprets this as evidence that decentralized coordination and shared memory of explored cells speed up search without a central controller.

Load-bearing premise

The load-bearing premise is that the simulator's three-level signal sections and grid abstractions faithfully represent a real GPS-denied indoor environment, so the trained policies will transfer to physical UAVs without retraining.

Editorial extensions

If this is right

A single drone with one signal sensor and one detection sensor can learn to locate a radiant target without GPS, using only sparse local observations and a coarse signal-strength reading.
Two decentralized drones sharing a grid map can match the single-drone success rate (86% vs 93%) while using fewer average steps (162.89 vs 183.82 at the best checkpoints), showing a speed benefit from cooperation.
Adding recurrent memory (LSTM) to PPO improves handling of partial observability, preventing the drone from getting stuck in local minima in the tested environments.
The method's low sensor and computation requirements make it a plausible candidate for search-and-rescue and environmental monitoring in buildings where GNSS/GPS is unavailable.
The authors expect that larger UAV swarms, trained with the same recipe, would reduce localization time further (scalability), although this is extrapolated beyond the two-drone experiments.

Reading between the lines

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

The reported accuracy gap between one drone (93%) and two drones (86%) is not explained in the paper; it could reflect added collision penalties, sparse training for cooperative behavior, or simply variance across the 100 test maps, and a confidence interval on the success rates would clarify whether the gap is real.
The scaling claim is untested beyond two agents; with more drones, shared grid coverage saturates and collision penalties may outweigh the marginal speedup, so the swarm benefit is not guaranteed by these results.
Because the signal is discretized into only three levels, the problem is close to a grid search with a proximity hint; a simpler non-learned baseline (e.g., an exploration algorithm guided by the signal gradient) would quantify how much the learned policy adds beyond standard coverage search.
The reward weights (e.g., +1000 target vs +2 exploration) likely dominate behavior; ablating reward components would show whether the signal cost or exploration bonus drives the good accuracy, and would help transfer the method to new sensor modalities.

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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: the paper is a readable, small simulation study that applies an existing method (Recurrent PPO, i.e. PPO with an LSTM) to single- and two-drone target localization in a custom grid world. What it does well: the problem setup is clear, the action/observation/reward design is explained, and the authors honestly compare DQN, DDPG, and PPO before picking PPO. The decentralized two-drone variant with a shared grid map is a reasonable extension, and the reported step reduction (183.82 to 162.89) is the kind of result you could build on. It is not a new algorithm or a reusable benchmark, and the novelty is modest, but the work is coherent on its own terms.

The soft spots are real and one is load-bearing. The headline "accuracy" is defined as reaching Section 1, the highest of three discrete signal-strength zones, which terminates the episode. That is a coarse region-entry rate, not a localization error. The paper never reports the physical size of Section 1, the grid-cell-to-meter conversion, or the final distance between the drone and the emitter. If Section 1 spans many cells, 93% success could mean the drone stops far from the actual target. That ambiguity undercuts the central claim and the stated application. A second soft spot is reproducibility: no code, hyperparameters, seeds, or error bars, so the numbers are plausible but unverifiable. The "scalable" claim rests on a two-drone experiment, which is fine as a first step but not much more. And the simulator has no sensor noise, continuous dynamics, or real-world validation, so transfer to physical UAVs is untested.

None of this makes the paper incoherent. The reward shaping directly encodes the evaluation objective, but that is standard in RL and not circular in the pejorative sense. The method is sensible and the writing is direct.

Who is this for? Someone wanting a quick baseline for RL-based UAV search in simulation, or a teaching example of recurrent PPO in a multi-agent grid setting. It deserves a serious referee, but only if the referee insists on the missing metric details and reproducibility items. My recommendation: do not desk reject, but send it to peer review with a clear request for the Section 1 size, final localization error, code/data, and a discussion of what "accuracy" physically means. Without those, the paper is a useful technical report, not a complete research claim.

Referee Report

4 major / 6 minor

Summary. This paper describes a reinforcement-learning approach to UAV target localization in a GNSS-denied indoor setting. The authors train a recurrent PPO agent with an LSTM on a discrete grid simulator; the observation includes eight surrounding obstacle states, eight map states, and a three-level signal strength. A two-agent variant adds four neighboring-drone indicators and a shared grid map. On 100 generated test environments, the single-drone model achieves a 93% success rate and the two-drone model an 86% success rate, with the two-drone model using fewer average steps (183.82 vs 162.89 at the selected checkpoints). The authors claim this demonstrates scalable decentralized target localization.

Significance. If the reported results are reproducible, the paper offers a useful, simple data point: a recurrent PPO policy can use coarse signal-strength sections and a visited-cell map to navigate toward a radiant target in a discrete simulator, and two decentralized agents can share a map to reduce mean steps. The train/test layout separation (training on eight generated maps, testing on ten new layouts with 100 environments) is a reasonable generalization check, and the use of a recurrent policy for partial observability is appropriate. The paper also makes a clear, falsifiable empirical claim about success rates and step counts. However, the absence of error bars, the coarse success metric, and the custom simulator limit the current significance; the contribution is more a proof-of-concept than a validated scalable framework.

major comments (4)

[Section II-A, Table III, and Section III, Eqs. (4)-(5)] The headline 'accuracy' is defined as reaching Section 1, a discrete signal-strength annulus, not as final localization error. The paper never reports the physical size of Section 1, the cell size, or the distance between the drone and the true target at termination. Consequently, a 93% 'accuracy' could correspond to entering a large region rather than localizing the emitter, and the two-drone step reduction (183.82 vs 162.89) could reflect faster region entry rather than more precise localization. This ambiguity directly affects the central claim and must be resolved by reporting section dimensions and terminal localization error.
[Section III, Tables V and VI] The comparison between single-drone and two-drone performance rests on single evaluations at selected training iterations, with no error bars, no multiple seeds, and no statistical test. The observed differences (93% vs 86% success; 183.82 vs 162.89 steps) may be within run-to-run noise. The authors should provide multiple independent training runs with confidence intervals or a significance test, or explicitly state that these are single-run results. Without this, the 'reduced steps' and 'scalable' claims are unsupported.
[Section II-C and Section II-F] The reward function directly encodes the success criterion: the target-reach reward is 1000, the signal-strength cost is 0-250, and success is defined as entering Section 1. This creates a risk that the reported performance reflects reward shaping rather than a learned, generalizable localization behavior. The reward weights and signal-strength boundaries are free parameters, but no ablation or sensitivity analysis is provided. An ablation with a minimally shaped reward, or at least a discussion of sensitivity to these parameters, is needed to support the generalization claim.
[Section II-A and Section II-F] All evaluation is in a custom grid simulator with three discrete signal sections and no sensor noise, continuous dynamics, or occlusion model. The paper states the application as 'perceptually degraded environments like places without GNSS/GPS signals' and 'complex environmental conditions,' but no argument or experiment connects the simulator abstractions to those conditions. The authors should either add validation on a higher-fidelity simulator or physical platform, or substantially narrow the claims and explicitly state the simulator's limitations in the paper. This is load-bearing because the abstract's claim of 'efficient and effective localization ... in complex environmental conditions' depends on simulator fidelity.

minor comments (6)

[Section III, Eq. (5)] Equation (5) is labeled 'average success rate' but the formula Tq/s computes the average number of steps for successful localizations; the notation and label should be corrected.
[Abstract and Section IV] The abstract reports fewer average steps for the two-drone model, and Table VI shows 162.89 at the 86% checkpoint, but the conclusion states 162.69; these numbers should be made consistent.
[Section II-F and Tables V-VI] The text says 'training continued for several million epochs,' while Tables V and VI list 'Training Iteration' values such as 3.8 mil and 4.15 mil; clarify whether these are steps, episodes, or epochs and use consistent terminology.
[Section II-D, Fig. 2] The model comparison in Fig. 2 is described qualitatively ('PPO yielded the highest mean reward'), but no quantitative results or hyperparameters for DQN, DDPG, and PPO are given; adding a table would make the selection reproducible.
[Section III, Eq. (4)] The variable h in Eq. (4) is not defined in the text; it appears to denote the number of successful localizations and should be stated explicitly.
[Section IV] The conclusion lists 'incorporating obstacles into the UAV-RL model state space' as future work, but Section II-A already includes obstacle states (State 1) in the observation; this should be clarified.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported success rates are empirical outcomes on held-out test maps, and the reward–evaluation overlap is a standard RL task definition, not a self-referential derivation.

full rationale

The paper is an empirical RL study rather than a derivation from first principles. The only definitional link is that 'Section 1 Highest signal strength, reaching this is considered reaching the target, terminating the task' (Table III) and the reward table includes a 'Target Reach Reward 1000' (Table IV), so the evaluation metric coincides with the training objective. This is a conventional task definition in RL, not a circular prediction: the reported 93% and 86% success rates are measured frequencies over 100 unique test scenarios generated from 10 maps distinct from the 80 training maps, so the headline numbers are not forced by the training reward. The two-drone step reduction (183.82 vs 162.89 average steps) is an empirical summary over successful episodes, not a fitted parameter renamed as a prediction. There are no load-bearing self-citations; reference [16] is an external description of recurrent PPO and is not used to import a uniqueness result or to forbid alternatives. The skeptic's concern that 'accuracy' measures entry into a coarse signal-strength section rather than fine-grained localization error is a legitimate construct-validity or correctness concern about the metric, but it is not circularity: the paper does not claim to derive localization precision from the section definition. No equation in the paper reduces to its own inputs, and no prediction is equivalent to a fitted quantity. Under the stated rules, the appropriate finding is no significant circularity with score 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The paper's dependence is on simulator assumptions and manually set reward and architecture parameters.

free parameters (4)

Reward weights = target reach 1000, obstacle -50, collision -50, unknown +2, twice -1, thrice -4, neighbor -2
Hand-chosen reward coefficients in Table IV shape the learned policy; their values affect success rates and step counts and are not reported as tuned or ablated.
Signal strength section boundaries
Three discrete signal sections in Table III define when the target is considered reached and when no signal is available; exact radii or readings are not specified and are set by the authors.
PPO hyperparameters
Learning rate, clip epsilon, GAE lambda, c1/c2, batch size, and number of environments are not reported; standard choices are implicitly assumed and can materially change results.
LSTM hidden units = 256
Hidden layer size is an architecture choice with no ablation justifying it.

assumptions (4)

domain assumption The custom simulator faithfully represents GPS-denied, visually degraded indoor environments
Section II describes only grid states and signal sections; no sensor noise, wind, dynamics, or validation against real flights is included.
domain assumption Discrete 8-direction translation on a grid captures UAV navigation sufficiently for localization
Action space in Section II-B ignores continuous control, altitude changes, and kinematic constraints.
domain assumption Three discrete signal strength sections provide enough information for localization
Section II-A and Table III reduce signal strength to bins; no continuous range or signal model is given.
domain assumption PPO with LSTM is a suitable model for partially observable MDPs
Borrowed from [16]; no proof or analysis specific to this domain, but standard result.

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Cite this review

Pith. "Pith review of A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO." pith.science (2026). https://pith.science/paper/ETACMNQD

@misc{pith2026241206231,
  author       = {Pith},
  title        = {Pith review of: A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ETACMNQD}},
  note         = {Machine review of arXiv:2412.06231}
}

read the original abstract

The rapid advancements in unmanned aerial vehicles (UAVs) have unlocked numerous applications, including environmental monitoring, disaster response, and agricultural surveying. Enhancing the collective behavior of multiple decentralized UAVs can significantly improve these applications through more efficient and coordinated operations. In this study, we explore a Recurrent PPO model for target localization in perceptually degraded environments like places without GNSS/GPS signals. We first developed a single-drone approach for target identification, followed by a decentralized two-drone model. Our approach can utilize two types of sensors on the UAVs, a detection sensor and a target signal sensor. The single-drone model achieved an accuracy of 93%, while the two-drone model achieved an accuracy of 86%, with the latter requiring fewer average steps to locate the target. This demonstrates the potential of our method in UAV swarms, offering efficient and effective localization of radiant targets in complex environmental conditions.

Figures

Figures reproduced from arXiv: 2412.06231 by the authors.

**Figure 3.** Proposed model architecture F. Training Process For both single-drone and multi-drone scenarios, the training involved creating diverse datasets through eight different simulation environments. By varying the positions of the drones and the targets, 80 distinct maps with different sizes were generated. In the multi-drone training, two drones were placed instead of one. The simulation environments were altered every… view at source ↗

**Figure 2.** Model performance comparison E. Proposed Model Architecture To enhance the selected PPO model, an LSTM layer with 256 hidden units was incorporated, utilizing a Recurrent PPO model as described in [16]. This addition aims to improve the model’s handling of partial Markov Decision Processes (MDPs). Any RL algorithm’s performance can be significantly be degraded due to the agent’s inability to fully capture the tempor… view at source ↗

Figures from the paper (2 more)

**Figure 5.** Figure 5: Evaluation graph - single drone simulation environ [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]

**Figure 6.** Figure 6: Evaluation graph - two drone simulation environment. [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Reviewed August 11, 2026 · model on record in the stance chip above.

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M. Atif, R. Ahmad, W. Ahmad, L. Zhao, and J. J. P. C. Rodrigues, “Uav-assisted wireless localization for search and rescue,” IEEE Systems Journal, vol. 15, pp. 3261–3272, 2021

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J. Wang, C. Jiang, Z. Han, Y . Ren, R. G. Maunder, and L. Hanzo, “Taking drones to the next level: Cooperative distributed unmanned- aerial-vehicular networks for small and mini drones,” IEEE V ehicular Technology Magazine, vol. 12, no. 3, pp. 73–82, 2017

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work page Pith review arXiv 2018

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H. Peng, A. Razi, F. Afghah, and J. Ashdown, “A unified framework for joint mobility prediction and object profiling of drones in uav networks,” Journal of Communications and Networks , vol. 20, pp. 434–442, 10 2018

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