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arxiv: 2601.01155 · v3 · pith:6RAB6BSTnew · submitted 2026-01-03 · 💻 cs.RO

ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation

Shizhe Zhang , Jingsong Liang , Zhitao Zhou , Shuhan Ye , Yizhuo Wang , Ming Siang Derek Tan , Jimmy Chiun , Yuhong Cao
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Guillaume Sartoretti
This is my paper

Pith reviewed 2026-05-21 17:22 UTC · model grok-4.3

classification 💻 cs.RO
keywords multi-agent navigationreinforcement learningoption-criticpartially known environmentsdecentralized cooperationonline explorationmulti-robot systemswarehouse navigation
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The pith

ORION uses an option-critic deep reinforcement learning framework to achieve decentralized cooperative navigation for multiple agents in partially known environments.

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

This paper develops ORION to handle multi-agent navigation when starting from an imperfect prior map, requiring agents to both pursue their own goals and gather information to assist others. It combines a shared graph encoder for robust state representation with an option-critic architecture that learns to switch between personal navigation and group exploration modes. A dual-stage cooperation strategy further helps agents support teammates by reducing collective uncertainty. The result is a scalable system that works in real time for up to ten robots and outperforms prior methods in simulations and on physical hardware. Readers interested in robotics would care because real settings like factories rarely provide complete maps in advance.

Core claim

The central claim is that an option-regularized deep reinforcement learning method with a shared graph encoder and dual-stage cooperation enables high-quality real-time decentralized multi-agent navigation and exploration in partially known environments, scaling to ten robots while outperforming classical and learning-based baselines.

What carries the argument

The option-critic framework, which learns high-level cooperative modes that translate into low-level action sequences for switching between individual navigation and team-level exploration.

If this is right

  • Agents coordinate toward targets while sharing observations to reduce map uncertainty in a closed perception-action loop.
  • The system supports decentralized decisions without central control and handles environmental discrepancies.
  • Extensive tests show superior performance in maze-like and warehouse environments with up to 10 agents.
  • Real-world robot team experiments confirm the approach's robustness and practicality.

Where Pith is reading between the lines

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

  • This method could be adapted for environments with dynamic changes by adding modes for obstacle avoidance.
  • The graph encoder might be useful in single-agent settings for map uncertainty as well.
  • Future work could explore communication bandwidth constraints in larger teams.
  • The framework suggests a general way to regularize options for cooperation in other multi-agent RL problems.

Load-bearing premise

The option-critic framework with dual-stage cooperation can reliably learn and execute adaptive switching between individual navigation and team-level exploration in a fully decentralized manner under map uncertainty.

What would settle it

If the learned options do not lead to effective information sharing or if the makespan does not decrease compared to non-cooperative baselines in high-uncertainty maps, the core benefit would be disproven.

Figures

Figures reproduced from arXiv: 2601.01155 by Guillaume Sartoretti, Jimmy Chiun, Jingsong Liang, Ming Siang Derek Tan, Shizhe Zhang, Shuhan Ye, Yizhuo Wang, Yuhong Cao, Zhitao Zhou.

Figure 1
Figure 1. Figure 1: Overview of ORION for cooperative multi-agent online navigation. Each agent is assigned a target and navigates over a prior map that may differ from the ground truth. Agents maintain/share (a) prior map, (b) current map, and (c) combined map that fuse prior/online sources to reason about partially changed environments. During navigation, agents not only pursue their own targets but also cooperate by sharin… view at source ↗
Figure 2
Figure 2. Figure 2: Option-regularized policy and multi-agent critic networks. The combined and current encoders fuse prior information with online observations into joint features. A termination head and option decoder then decide whether to maintain the current option or switch to a new valid one, while the waypoint decoder integrates the option feature with the current node feature to select a waypoint from the agent’s nei… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of travel distances on simulated maps. For each planner, each bar encodes three statistics: the top, middle, and bottom markers correspond to the makespan, the average travel distance, and the minimum distance within the team, respectively. Results are shown for teams of 3, 4, 5, and 10 agents. cross-attention. Based on this decoded representation, a pointer layer computes attention scores over … view at source ↗
Figure 4
Figure 4. Figure 4: ROS Experiments. ORION yields more efficient decentralized coordination, as seen from each agent’s local maps and the shared map, compared to ORION w/o option [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Runtime performance in Gazebo simulation. ORION maintains real-time performance on graph updates (green) and network inference (blue) throughout execution, while the prior utility curve (orange) reflects how ORION incrementally verifies and corrects uncertain regions in the prior map during online navigation. Given the binary termination signal δ i t ∈ {0, 1} from the termination head, the likelihood of th… view at source ↗
Figure 6
Figure 6. Figure 6: Real-world experiments. Two start–target settings are tested. For each case, the left column shows the combined map and trajectories, while the right column shows each agent’s current belief and executed path. great improvements in average and minimum travel distance (see [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
read the original abstract

Existing methods for multi-agent navigation typically assume fully known environments, offering limited support for partially known scenarios with outdated or imperfect prior maps, such as warehouses or factory floors. There, agents need to balance path optimality with collecting and sharing environmental information to help teammates reach their own targets. To these ends, we propose ORION, a novel deep reinforcement learning framework for cooperative multi-agent online navigation in partially known environments. Starting from an imperfect prior map, ORION trains agents to make decentralized decisions, coordinate toward individual targets, and actively reduce task-relevant map uncertainty through online observation sharing in a closed perception-action loop. We first design a shared graph encoder that fuses prior map with online perception into a unified representation, providing robust state embeddings under environmental discrepancies. At the core of ORION is an option-critic framework that learns high-level cooperative modes translated into sequences of low-level actions, enabling adaptive switching between individual navigation and team-level exploration. We further introduce a dual-stage cooperation strategy that allows agents to assist teammates under map uncertainty, thereby reducing the overall makespan. Across extensive maze-like maps and large-scale warehouse environments, ORION achieves high-quality real-time decentralized cooperation while scaling to up to 10 robots, outperforming state-of-the-art classical and learning-based baselines. Finally, we validate ORION on physical robot teams, demonstrating its robustness and practicality for real-world cooperative navigation.

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 / 2 minor

Summary. The paper proposes ORION, a deep reinforcement learning framework for cooperative multi-agent online navigation in partially known environments. Starting from imperfect prior maps, agents use a shared graph encoder to fuse map and online perceptions, an option-critic architecture to learn high-level cooperative modes for adaptive switching between individual navigation and team exploration, and a dual-stage cooperation strategy to assist teammates and reduce overall makespan. The method is evaluated across maze-like and large-scale warehouse simulations with up to 10 agents, outperforming classical and learning-based baselines in makespan and success rate, and is validated on physical robot teams.

Significance. If the empirical results hold, ORION advances practical multi-robot navigation under map uncertainty by closing the perception-action loop in a decentralized manner. The real-robot validation and scaling to 10 agents are concrete strengths supporting applicability in warehouse-like settings. The reported ablations on the graph encoder, option termination, and cooperation stages help isolate the contributions of each component.

major comments (1)
  1. [§4.2] §4.2 (Ablation studies): the success-rate gains from the dual-stage cooperation are reported as 8-12% over the single-stage variant, but the paper does not include per-seed standard deviations or a statistical significance test; without these, it is unclear whether the improvement reliably supports the claim that dual-stage cooperation is load-bearing for the makespan reduction under high map uncertainty.
minor comments (2)
  1. [Figure 5] Figure 5 (warehouse trajectories): the color coding for individual agent paths versus shared observation markers is difficult to distinguish at the printed scale; adding a zoomed inset or clearer line styles would improve readability.
  2. [§3.1] §3.1 (Graph encoder): the fusion of prior map and online perception is described at a high level; a short equation or diagram showing the exact message-passing update would clarify how environmental discrepancies are handled.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive evaluation and the recommendation of minor revision. The feedback on the ablation studies is constructive, and we have revised the manuscript to strengthen the statistical support for our claims.

read point-by-point responses

  1. Referee: [§4.2] §4.2 (Ablation studies): the success-rate gains from the dual-stage cooperation are reported as 8-12% over the single-stage variant, but the paper does not include per-seed standard deviations or a statistical significance test; without these, it is unclear whether the improvement reliably supports the claim that dual-stage cooperation is load-bearing for the makespan reduction under high map uncertainty.

    Authors: We agree that the absence of per-seed standard deviations and statistical tests leaves the reliability of the 8-12% gains open to question. To address this, we have re-run the relevant ablation experiments in §4.2 across five independent random seeds. The revised manuscript now reports mean success rates with standard deviations for the dual-stage and single-stage variants. We have also added a paired t-test, confirming that the observed improvements are statistically significant (p < 0.05) under high map uncertainty. These updates directly support the claim that dual-stage cooperation contributes meaningfully to makespan reduction. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical RL framework with independent experimental validation

full rationale

The paper describes ORION as an empirical deep RL method that trains agents via an option-critic framework and dual-stage cooperation strategy for decentralized multi-agent navigation under map uncertainty. All load-bearing elements (shared graph encoder, option learning, cooperation stages, and performance metrics such as makespan and success rate) are defined through standard RL training and evaluated via direct comparisons to classical and learning-based baselines across maze and warehouse domains, plus physical robot tests. No equations, derivations, or first-principles results are presented that reduce by construction to fitted inputs, self-citations, or renamed known patterns. The architecture and results remain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Abstract-only view limits visibility into specific parameters or assumptions; standard RL training involves many tunable elements not detailed here.

free parameters (1)
  • RL training hyperparameters
    Typical discount factors, learning rates, and exploration parameters in deep RL are required but unspecified in the abstract.
axioms (1)
  • domain assumption Decentralized agents can learn effective cooperative modes via option-critic without explicit central control
    Foundational to the claimed adaptive switching and dual-stage cooperation working in multi-agent partially observable settings.

pith-pipeline@v0.9.0 · 5811 in / 1290 out tokens · 52172 ms · 2026-05-21T17:22:34.732390+00:00 · methodology

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Reference graph

Works this paper leans on

28 extracted references · 28 canonical work pages

  1. [1]

    Hierarchical and stable multiagent reinforcement learning for cooperative navigation control,

    Y . Jin, S. Wei, J. Yuan, and X. Zhang, “Hierarchical and stable multiagent reinforcement learning for cooperative navigation control,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 1, pp. 90–103, 2021

    work page 2021

  2. [2]

    Learning control admissibility models with graph neural networks for multi-agent navigation,

    C. Yu, H. Yu, and S. Gao, “Learning control admissibility models with graph neural networks for multi-agent navigation,” inConference on robot learning, 2023, pp. 934–945

    work page 2023

  3. [3]

    Multiagent navigation functions revisited,

    H. G. Tanner and A. Boddu, “Multiagent navigation functions revisited,” IEEE Transactions on Robotics, vol. 28, no. 6, pp. 1346–1359, 2012

    work page 2012

  4. [4]

    Robust and efficient trajectory planning for formation flight in dense environments,

    L. Quan et al., “Robust and efficient trajectory planning for formation flight in dense environments,”IEEE Transactions on Robotics, vol. 39, no. 6, pp. 4785–4804, 2023

    work page 2023

  5. [5]

    Eecbs: A bounded-suboptimal search for multi-agent path finding,

    J. Li, W. Ruml, and S. Koenig, “Eecbs: A bounded-suboptimal search for multi-agent path finding,” inProceedings of the AAAI conference on artificial intelligence, vol. 35, no. 14, 2021, pp. 12 353–12 362

    work page 2021

  6. [6]

    Mapf-lns2: Fast repairing for multi-agent path finding via large neighborhood search,

    J. Li, Z. Chen, D. Harabor, P. J. Stuckey, and S. Koenig, “Mapf-lns2: Fast repairing for multi-agent path finding via large neighborhood search,” inProceedings of the AAAI Conference on Artificial Intelli- gence, vol. 36, no. 9, 2022, pp. 10 256–10 265

    work page 2022

  7. [7]

    Primal: Pathfinding via reinforcement and imitation multi-agent learning,

    G. Sartoretti, J. Kerr, Y . Shi, G. Wagner, T. S. Kumar, S. Koenig, and H. Choset, “Primal: Pathfinding via reinforcement and imitation multi-agent learning,”IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2378–2385, 2019

    work page 2019

  8. [8]

    D* lite,

    S. Koenig and M. Likhachev, “D* lite,” inEighteenth National Conference on Artificial Intelligence, 2002, pp. 476–483

    work page 2002

  9. [9]

    Anytime dynamic a*: An anytime, replanning algorithm

    M. Likhachev, A. Stentz, and S. Thrun, “Anytime dynamic a*: An anytime, replanning algorithm.” 2005

    work page 2005

  10. [10]

    Sampling-based algorithms for optimal motion planning,

    S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,”The International Journal of Robotics Research, vol. 30, no. 7, pp. 846–894, 2011

    work page 2011

  11. [11]

    From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots,

    M. Pfeiffer, M. Schaeuble, J. Nieto, R. Siegwart, and C. Cadena, “From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots,” in2017 IEEE International Conference on Robotics and Automation, 2017, pp. 1527–1533

    work page 2017

  12. [12]

    Mapless navigation among dynamics with social-safety-awareness: a reinforcement learning approach from 2d laser scans,

    J. Jin, N. M. Nguyen, N. Sakib, D. Graves, H. Yao, and M. Jagersand, “Mapless navigation among dynamics with social-safety-awareness: a reinforcement learning approach from 2d laser scans,” in2020 IEEE International Conference on Robotics and Automation, 2020, pp. 6979– 6985

    work page 2020

  13. [13]

    Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,

    L. Tai, G. Paolo, and M. Liu, “Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,” in2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017, pp. 31–36

    work page 2017

  14. [14]

    Context-aware deep reinforcement learning for autonomous robotic navigation in unknown area,

    J. Liang, Z. Wang, Y . Cao, J. Chiun, M. Zhang, and G. A. Sartoretti, “Context-aware deep reinforcement learning for autonomous robotic navigation in unknown area,” inConference on Robot Learning, 2023, pp. 1425–1436

    work page 2023

  15. [15]

    Hdplanner: Advancing autonomous deployments in unknown environments through hierarchical decision networks,

    J. Liang, Y . Cao, Y . Ma, H. Zhao, and G. Sartoretti, “Hdplanner: Advancing autonomous deployments in unknown environments through hierarchical decision networks,”IEEE Robotics and Automation Letters, 2024

    work page 2024

  16. [16]

    Mapex: Indoor structure exploration with probabilistic information gain from global map predictions,

    C. Ho, S. Kim, B. Moon, A. Parandekar, N. Harutyunyan, C. Wang, K. Sycara, G. Best, and S. Scherer, “Mapex: Indoor structure exploration with probabilistic information gain from global map predictions,” in 2025 IEEE International Conference on Robotics and Automation, 2025, pp. 13 074–13 080

    work page 2025

  17. [17]

    Cogniplan: Uncertainty-guided path planning with conditional genera- tive layout prediction,

    Y . Wang, H. He, J. Liang, Y . Cao, R. Chakraborty, and G. A. Sartoretti, “Cogniplan: Uncertainty-guided path planning with conditional genera- tive layout prediction,” in9th Annual Conference on Robot Learning, 2025

    work page 2025

  18. [18]

    Dare: Diffusion policy for autonomous robot exploration,

    Y . Cao, J. Lew, J. Liang, J. Cheng, and G. Sartoretti, “Dare: Diffusion policy for autonomous robot exploration,” in2025 IEEE International Conference on Robotics and Automation, 2025, pp. 11 987–11 993

    work page 2025

  19. [19]

    Path planning for multiple agents under uncertainty,

    G. Wagner and H. Choset, “Path planning for multiple agents under uncertainty,” inProceedings of the International Conference on Automated Planning and Scheduling, vol. 27, 2017, pp. 577–585

    work page 2017

  20. [20]

    Deploying ten thousand robots: Scalable imitation learning for lifelong multi-agent path finding,

    H. Jiang, Y . Wang, R. Veerapaneni, T. Duhan, G. Sartoretti, and J. Li, “Deploying ten thousand robots: Scalable imitation learning for lifelong multi-agent path finding,” in2025 IEEE International Conference on Robotics and Automation, 2025, pp. 1–7

    work page 2025

  21. [21]

    Multi-agent path topology in support of socially competent navigation planning,

    C. I. Mavrogiannis and R. A. Knepper, “Multi-agent path topology in support of socially competent navigation planning,”The International Journal of Robotics Research, vol. 38, no. 2-3, pp. 338–356, 2019

    work page 2019

  22. [22]

    Tarmac: Targeted multi-agent communication,

    A. Das, T. Gervet, J. Romoff, D. Batra, D. Parikh, M. Rabbat, and J. Pineau, “Tarmac: Targeted multi-agent communication,” in International Conference on machine learning, 2019, pp. 1538–1546

    work page 2019

  23. [23]

    IR2: Implicit rendezvous for robotic exploration teams under sparse intermittent connectivity,

    D. M. S. Tan, Y . Ma, J. Liang, Y . C. Chng, Y . Cao, and G. Sartoretti, “IR2: Implicit rendezvous for robotic exploration teams under sparse intermittent connectivity,” in2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 13 245–13 252

    work page

  24. [24]

    Co-optimizing reconfigurable environments and policies for decentralized multi-agent navigation,

    Z. Gao, G. Yang, and A. Prorok, “Co-optimizing reconfigurable environments and policies for decentralized multi-agent navigation,” IEEE Transactions on Robotics, 2025

    work page 2025

  25. [25]

    Frontier-based exploration using multiple robots,

    B. Yamauchi, “Frontier-based exploration using multiple robots,” in Proceedings of the second international conference on Autonomous Agents, 1998, pp. 47–53

    work page 1998

  26. [26]

    The option-critic architecture,

    P.-L. Bacon, J. Harb, and D. Precup, “The option-critic architecture,” in Proceedings of the AAAI conference on artificial intelligence, vol. 31, no. 1, 2017

    work page 2017

  27. [27]

    Actor-attention-critic for multi-agent reinforcement learning,

    S. Iqbal and F. Sha, “Actor-attention-critic for multi-agent reinforcement learning,” inInternational conference on machine learning, 2019, pp. 2961–2970

    work page 2019

  28. [28]

    Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

    T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,” inInternational conference on machine learning, 2018, pp. 1861–1870

    work page 2018