The reviewed record of science sign in
Pith

arxiv: 2606.23585 · v1 · pith:JM2VWYBW · submitted 2026-06-22 · cs.MA · cs.AI· cs.ET· cs.RO· cs.SY· eess.SY

Decentralized Autonomous Traffic Management through Corridor Networks

Jasmine Jerry Aloor , Aadarsh Govada , Hamsa Balakrishnan This is my paper

Reviewed by Pith2026-06-26 05:44 UTCgrok-4.3pith:JM2VWYBWopen to challenge →

classification cs.MA cs.AIcs.ETcs.ROcs.SYeess.SY
keywords decentralized autonomous traffic managementmulti-agent reinforcement learningAAM corridorsair corridor networkszero-shot generalizationtraffic flow managementautonomous aircraftMARL
0
0 comments X

The pith

Decentralized multi-agent policies manage traffic flows in air corridor networks without central control.

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

The paper extends multi-agent reinforcement learning to decentralized management of autonomous aircraft in networks of AAM corridors. It shows that policies trained only on single corridors can be applied without retraining to more complex setups involving merges and splits. These policies maintain safe operations under different traffic densities and vehicle types by using only local coordination. This approach could allow traffic to scale without relying on a central controller.

Core claim

By training multi-agent reinforcement learning agents in a single-corridor environment, the resulting policies can be deployed directly onto multi-corridor networks. The agents learn behaviors for entering, traversing, and exiting corridors that, when executed locally, produce overall traffic that respects boundaries, completes journeys at high rates, keeps aircraft separated, and achieves reasonable speeds even when densities, geometries, and vehicle capabilities vary.

What carries the argument

Multi-agent reinforcement learning policies trained for local corridor entry, traversal, and exit behaviors.

If this is right

  • The approach scales to networks with merges and splits.
  • Performance is robust to changes in traffic density and network geometry.
  • Heterogeneous vehicles can be accommodated without retraining.
  • Desirable global traffic flows arise from local behaviors alone.

Where Pith is reading between the lines

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

  • Decentralized methods may lower the infrastructure requirements for managing large numbers of autonomous aircraft.
  • The zero-shot transfer property suggests similar techniques could be tested in other multi-agent flow problems such as ground vehicle routing.
  • Further validation in higher-fidelity simulators or real-world settings would be needed to confirm transfer beyond the training simulations.

Load-bearing premise

The simulation environments used for training and testing capture the essential dynamics, sensing limitations, and failure modes of real autonomous aircraft operating inside physical corridors.

What would settle it

A physical flight test in which the policies cause aircraft to violate corridor boundaries or fail to maintain required separation distances would falsify the claim of reliable transfer.

Figures

Figures reproduced from arXiv: 2606.23585 by Aadarsh Govada, Hamsa Balakrishnan, Jasmine Jerry Aloor.

Figure 1
Figure 1. Figure 1: Schematic of a corridor network, showing the layout of [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Single-corridor scenario (adapted from [4]). [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Double-merge corridor geometry. TABLE IV. Performance metrics for the double-merge corri￾dor scenario. # Conformance C% Completion rate S% Avg. speed (knots) Tactical intervention I% 10 99% 96% 171.9 4.76% 20 99% 92% 172.2 4.63% 30 98% 89% 172.3 5.16% 40 98% 88% 168.2 5.78% c) Split and merge: Aircraft first diverge into paral￾lel corridors and subsequently merge into a single down￾stream flow, introducing… view at source ↗
Figure 3
Figure 3. Figure 3: Merge corridor geometry. b) Double merge: Two sequential merging points create compounded interaction effects and increased local traffic density. This scenario includes 5 corridor segments. (See [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Split–merge corridor geometry. interaction frequency near merging points, resulting in some aircraft deviating significantly from their route in order to maintain separation. Consequently, especially for aircraft that are later in the flow, they may not be able to reach their exit before the episode ends. Average speeds remain relatively stable across traffic levels, with only modest reductions in the most… view at source ↗
Figure 6
Figure 6. Figure 6: Combined corridor scenario built using merges, splits, and [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: (a, b) AAM corridor navigation performance in the combined corridor scenario of 18 total corridors. The plots show each agent’s [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: (a, b) AAM corridor navigation performance in the combined corridor scenario of 18 total corridors with heterogeneous speed [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
read the original abstract

As autonomous aircraft are introduced at scale and traffic density increases, centralized management becomes insufficient to coordinate the large numbers of crewed and uncrewed aircraft. Dedicated Advanced Air Mobility (AAM) corridors have therefore been proposed for organizing high-density autonomous traffic flows. The desire to scalably provide autonomous aircraft flexibility in trajectory planning motivates the development of decentralized approaches to traffic management in AAM corridors. In this work, we extend a multi-agent reinforcement learning (MARL) approach to address the challenge of decentralized traffic flow management in air corridor networks. We test policies trained in a single-corridor setting on increasingly complex multi-corridor networks with combinations of merges and splits in a zero-shot manner. Experimental results demonstrate that learned behaviors transfer well to scenarios with varying traffic density, network geometry, and heterogeneous vehicle performance, without needing centralized coordination or model retraining. We evaluate system-level performance in terms of conformance to corridor boundaries, completion rates, average speeds, distance traveled, and maintenance of inter-aircraft separation. We find that although our policies require only locally coordinated entry, traversal, and exit behaviors, they collectively produce desirable traffic flows through the corridor network.

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

2 major / 0 minor

Summary. The paper extends multi-agent reinforcement learning (MARL) to decentralized traffic management in Advanced Air Mobility (AAM) corridor networks. It claims that policies trained in single-corridor settings transfer zero-shot to multi-corridor networks involving merges and splits, under varying traffic density, geometry, and heterogeneous vehicle performance, without centralized coordination or retraining. System-level performance is reported on metrics including corridor boundary conformance, completion rates, average speeds, distance traveled, and inter-aircraft separation, with the collective behaviors producing desirable network flows from local entry/traversal/exit policies.

Significance. If the zero-shot transfer results hold with full methodological transparency and validated simulation fidelity, the work would demonstrate a scalable decentralized alternative to centralized AAM traffic management, showing that locally trained MARL policies can generalize across network topologies and conditions without retraining. This would be a notable contribution to multi-agent systems for autonomous aviation if supported by reproducible experiments.

major comments (2)
  1. [Abstract and Methods] The abstract and manuscript description state positive transfer results on multiple metrics but supply no training details, reward functions, network architectures, statistical tests, or ablation studies. This absence makes the central claim of successful zero-shot transfer unverifiable.
  2. [Simulation and Experimental Setup] The central claim requires that policies generalize from single-corridor to multi-corridor settings, but no evidence is provided of simulator calibration against real AAM flight data, wind models, sensor limitations, or hardware-in-the-loop tests. Any mismatch in corridor boundary enforcement or separation physics would invalidate the reported conformance, completion, and separation metrics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting areas where methodological transparency and scope clarification can be strengthened. We address each major comment below and will revise the manuscript to improve verifiability while maintaining the focus of the work as a simulation study of zero-shot MARL transfer.

read point-by-point responses

  1. Referee: [Abstract and Methods] The abstract and manuscript description state positive transfer results on multiple metrics but supply no training details, reward functions, network architectures, statistical tests, or ablation studies. This absence makes the central claim of successful zero-shot transfer unverifiable.

    Authors: We agree that the absence of these details limits verifiability. In the revised manuscript we will add a dedicated Methods subsection detailing the reward function formulation, policy network architectures (including layer sizes and activation functions), training hyperparameters and algorithms, statistical tests used for metric comparisons, and ablation studies isolating the effects of key design choices. These additions will directly support reproduction of the reported zero-shot transfer results. revision: yes

  2. Referee: [Simulation and Experimental Setup] The central claim requires that policies generalize from single-corridor to multi-corridor settings, but no evidence is provided of simulator calibration against real AAM flight data, wind models, sensor limitations, or hardware-in-the-loop tests. Any mismatch in corridor boundary enforcement or separation physics would invalidate the reported conformance, completion, and separation metrics.

    Authors: The work is conducted entirely in simulation and does not include real-world calibration or hardware validation. We will add an explicit Limitations subsection that states the simulation assumptions (idealized corridor boundaries, perfect state information, no wind or sensor noise) and discusses how mismatches with real AAM physics could affect the reported metrics. This clarifies the scope without overstating generalizability. We cannot supply calibration data because none was collected. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical simulation results are independent of inputs

full rationale

The paper reports outcomes of MARL policy training in single-corridor environments followed by zero-shot evaluation on multi-corridor networks. Performance metrics (conformance, completion rates, separation) are measured directly from the simulator runs rather than derived via equations, parameter fits, or self-citations that reduce to the training data by construction. No load-bearing self-citations, uniqueness theorems, or ansatzes are invoked; the transfer claim rests on experimental evidence whose validity depends on simulator fidelity but is not circular within the paper's own derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Central claim depends on simulation fidelity and the assumption that local MARL policies suffice for global network flow; no free parameters, invented entities, or non-standard axioms are explicitly introduced in the abstract.

axioms (1)
  • domain assumption Aircraft dynamics and sensing in the corridor environment are Markovian and locally observable.
    Standard assumption for applying MARL to traffic control problems.

pith-pipeline@v0.9.1-grok · 5751 in / 1111 out tokens · 21670 ms · 2026-06-26T05:44:33.358686+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

30 extracted references · 3 canonical work pages · 1 internal anchor

  1. [1]

    The Advanced Air Mobility National Strategy: A Bold Policy Vision for 2025–2035,

    US Department of Transportation Advanced Air Mobility Interagency Working Group, “The Advanced Air Mobility National Strategy: A Bold Policy Vision for 2025–2035,” tech. rep., US Department of Transportation, Washington, DC, December 2025. 1, 2

    2025

  2. [2]

    The Advanced Air Mobility Comprehensive Plan: LIFTing AAM to Maturity in the United States,

    US Department of Transportation Advanced Air Mobility Interagency Working Group, “The Advanced Air Mobility Comprehensive Plan: LIFTing AAM to Maturity in the United States,” tech. rep., US Department of Transportation, Washington, DC, December 2025. 1, 2

    2025

  3. [3]

    Scientific Assess- ment for Urban Air Mobility (UAM),

    B. I. Schuchardt, A. Andreeva-Mori, P. H. Kopardekar, V . Kramar, J. Murphy, M. C. Rocha Murc ¸a, and D. Szirocz ´ak, “Scientific Assess- ment for Urban Air Mobility (UAM),”CEAS Aeronautical Journal,

  4. [4]

    Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors,

    J. J. Aloor and H. Balakrishnan, “Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors,” in AIAA SCITECH 2026 Forum, American Institute of Aeronautics and Astronautics, 2026. 1, 2, 3, 4, 5

    2026

  5. [5]

    Design and Evaluation of a Corridors-in-the-Sky Concept: The Benefits and Feasibility of Adding Highly Structured Routes to a Mixed Equipage Environment,

    J. Homola, P. Lee, H. Lee, C. Brasil, S. Gregg, M. Mainini, L. Martin, C. Cabrall, J. Mercer, and T. Prevot, “Design and Evaluation of a Corridors-in-the-Sky Concept: The Benefits and Feasibility of Adding Highly Structured Routes to a Mixed Equipage Environment,” inAIAA Guidance, Navigation, and Control Conference, 2012. 1, 2

    2012

  6. [6]

    Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation,

    S. Nayak, K. Choi, W. Ding, S. Dolan, K. Gopalakrishnan, and H. Bal- akrishnan, “Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation,” inProceedings of the 40th Inter- national Conference on Machine Learning, pp. 25817–25833, PMLR, July 2023. 2, 3

    2023

  7. [7]

    Hybrid Transformer Based Multi-Agent Reinforcement Learning for Multiple Unmanned Aerial Vehicle Coordination in Air Corridors,

    L. Yu, Z. Li, N. Ansari, and X. Sun, “Hybrid Transformer Based Multi-Agent Reinforcement Learning for Multiple Unmanned Aerial Vehicle Coordination in Air Corridors,”IEEE Transactions on Mobile Computing, 2025. 2, 3

    2025

  8. [8]

    Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks,

    M. W. Brittain, L. E. Alvarez, and K. Breeden, “Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks,”arXiv preprint arXiv:2308.04958, 2023. 2, 3

    work page arXiv 2023

  9. [9]

    Scalable Autonomous Separation Assur- ance with Heterogeneous Multi-Agent Reinforcement Learning,

    M. Brittain and P. Wei, “Scalable Autonomous Separation Assur- ance with Heterogeneous Multi-Agent Reinforcement Learning,”IEEE Transactions on Automation Science and Engineering, vol. 19, no. 4, pp. 2837–2848, 2022. 2, 3

    2022

  10. [10]

    Urban Air Mobility (UAM) Concept of Operations Version 2.0,

    Federal Aviation Administration, “Urban Air Mobility (UAM) Concept of Operations Version 2.0,” tech. rep., Federal Aviation Administration, Washington, DC, April 2023. 2

    2023

  11. [11]

    Enabling Civilian Low-Altitude Airspace and Un- manned Aerial System (UAS) Operations,

    P. Kopardekar, “Enabling Civilian Low-Altitude Airspace and Un- manned Aerial System (UAS) Operations,” tech. rep., NASA, 2014. 2

    2014

  12. [12]

    SESAR Joint Undertaking|U-space CONOPS 4th Edition,

    EUROCONTROL, “SESAR Joint Undertaking|U-space CONOPS 4th Edition,” tech. rep., European Union, 2023. 2

    2023

  13. [13]

    Hybrid Control in Air Traffic Management Systems,

    C. Tomlin, G. Pappas, J. Lygeros, D. Godbole, S. Sastry, and G. Meyer, “Hybrid Control in Air Traffic Management Systems,”IFAC Proceed- ings Volumes, vol. 29, no. 1, pp. 5512–5517, 1996. 2

    1996

  14. [14]

    A Distributed Framework for Traffic Flow Management in the Presence of Unmanned Aircraft,

    H. Balakrishnan and B. Chandran, “A Distributed Framework for Traffic Flow Management in the Presence of Unmanned Aircraft,” in USA/Europe Air Traffic Management R&D Seminar, 2017. 2

    2017

  15. [15]

    Analysis of Traffic Flow in Structured Urban Airspace Networks with MFD-Based Feedback Control,

    K. Chour, P. Razzaghi, D. Verma, M. Xue, A. Munishkin, and K. Kalyanam, “Analysis of Traffic Flow in Structured Urban Airspace Networks with MFD-Based Feedback Control,” inAIAA AVIATION Forum and ASCEND, 2024. 2

    2024

  16. [16]

    Decentralized Control Synthesis for Air Traffic Management in Urban Air Mobility,

    S. Bharadwaj, S. Carr, N. Neogi, and U. Topcu, “Decentralized Control Synthesis for Air Traffic Management in Urban Air Mobility,”IEEE Transactions on Control of Network Systems, vol. 8, no. 2, pp. 598–608,

  17. [17]

    Design and Analysis of Corridors for UAM Opera- tions,

    S. Verma, V . Dulchinos, R. D. Wood, A. Farrahi, R. Mogford, M. Shyr, and R. Ghatas, “Design and Analysis of Corridors for UAM Opera- tions,” in2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), pp. 1–10, 2022. 2

    2022

  18. [18]

    Lane Geometry, Compliance Levels, and Adaptive Geo-fencing in CORRIDRONE Architecture for Urban Mobility,

    L. A. Tony, A. Ratnoo, and D. Ghose, “Lane Geometry, Compliance Levels, and Adaptive Geo-fencing in CORRIDRONE Architecture for Urban Mobility,” in2021 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1611–1617, 2021. 2

    2021

  19. [19]

    Air Corridor Planning for Urban Drone Delivery: Complexity Analysis and Comparison via Multi-Commodity Network Flow and Graph Search,

    X. He, L. Li, Y . Mo, Z. Sun, and S. J. Qin, “Air Corridor Planning for Urban Drone Delivery: Complexity Analysis and Comparison via Multi-Commodity Network Flow and Graph Search,”Transportation Research Part E: Logistics and Transportation Review, vol. 193, p. 103859, 2025. 2

    2025

  20. [20]

    Adaptive Traffic- Following Scheme for Orderly Distributed Control of Multi-Vehicle Systems,

    A. Jain, H. Idris, J.-P. Clarke, and D. Delahaye, “Adaptive Traffic- Following Scheme for Orderly Distributed Control of Multi-Vehicle Systems,”arXiv preprint arXiv:2506.00703, 2025. 3

    work page arXiv 2025

  21. [21]

    Enhanced Self-Separation Decision Making for Autonomous Flight Operations in Air Corridors,

    Z. Liu, M. Wang, and Z. Liu, “Enhanced Self-Separation Decision Making for Autonomous Flight Operations in Air Corridors,”Journal of Air Transport Management, vol. 124, p. 102721, 2025. 3

    2025

  22. [22]

    Investigation of Merge Assist Policies to Improve Safety of Drone Traffic in a Constrained Urban Airspace,

    M. Doole, J. Ellerbroek, and J. M. Hoekstra, “Investigation of Merge Assist Policies to Improve Safety of Drone Traffic in a Constrained Urban Airspace,”Aerospace, vol. 9, no. 3, 2022. 3

    2022

  23. [23]

    Advanced Convoy Control Strategy for Autonomously Driven Railway Vehicles,

    C. Henke, N. Frohleke, and J. Bocker, “Advanced Convoy Control Strategy for Autonomously Driven Railway Vehicles,” inIEEE Intelli- gent Transportation Systems Conference, 2006. 3

    2006

  24. [24]

    Platoon Formation: Optimized Car to Platoon Assignment Strategies and Protocols,

    J. Heinovski and F. Dressler, “Platoon Formation: Optimized Car to Platoon Assignment Strategies and Protocols,” inIEEE Vehicular Networking Conference (VNC), 2018. 3

    2018

  25. [25]

    Strategic Hub-Based Platoon Coordination Under Uncertain Travel Times,

    A. Johansson, E. Nekouei, K. H. Johansson, and J. M ˚artensson, “Strategic Hub-Based Platoon Coordination Under Uncertain Travel Times,”IEEE Transactions on Intelligent Transportation Systems, 2022. 3

    2022

  26. [26]

    A Review of Truck Pla- tooning Projects for Energy Savings,

    S. Tsugawa, S. Jeschke, and S. E. Shladover, “A Review of Truck Pla- tooning Projects for Energy Savings,”IEEE Transactions on Intelligent Vehicles, 2016. 3

    2016

  27. [27]

    Resolving Conflicting Constraints in Multi-Agent Rein- forcement Learning with Layered Safety,

    J. J. Choi, J. J. Aloor, J. Li, M. G. Mendoza, H. Balakrishnan, and C. J. Tomlin, “Resolving Conflicting Constraints in Multi-Agent Rein- forcement Learning with Layered Safety,” inProceedings of Robotics: Science and Systems, June 2025. 3, 9

    2025

  28. [28]

    HMARL-CBF–Hierarchical multi- agent reinforcement learning with control barrier functions for safety- critical autonomous systems,

    H. Ahmad, E. Sabouni, A. Wasilkoff, P. Budhraja, Z. Guo, S. Zhang, C. Fan, C. Cassandras, and W. Li, “HMARL-CBF–Hierarchical multi- agent reinforcement learning with control barrier functions for safety- critical autonomous systems,”Advances in Neural Information Process- ing Systems, vol. 38, pp. 82284–82317, 2026. 3, 9

    2026

  29. [29]

    Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination

    M. Low, J. J. Aloor, V . M. Tuck, P. Nuzzo, and J. J. Choi, “Time- To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination,”arXiv preprint arXiv:2605.20625, 2026. 3, 9

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  30. [30]

    Interactive supercomputing on 40,000 cores for machine learning and data analysis,

    A. Reuther, J. Kepner, C. Byun, S. Samsi, W. Arcand, D. Bestor, B. Bergeron, V . Gadepally, M. Houle, M. Hubbell, M. Jones, A. Klein, L. Milechin, J. Mullen, A. Prout, A. Rosa, C. Yee, and P. Michaleas, “Interactive supercomputing on 40,000 cores for machine learning and data analysis,” in2018 IEEE High Performance extreme Computing Conference (HPEC), pp....

    2018