Title: Heterogeneous Graph Transformers for Simultaneous Mobile Multi-Robot Task Allocation and Scheduling under Temporal Constraints
Venue: NeurIPS 2025
Accepted: yes
Keywords: Task Allocation and Scheduling

Multi-Agent Coordination

Graph Neural Networks
Abstract: Coordinating large teams of heterogeneous mobile agents to perform complex tasks efficiently has scalability bottlenecks in feasible and optimal task scheduling, with critical applications in logistics, manufacturing, and disaster response. Existing task allocation and scheduling methods, including heuristics and optimization-based solvers, often fail to scale and overlook inter-task dependencies and agent heterogeneity. We propose a novel Simultaneous Decision-Making model for Heterogeneous Multi-Agent Task Allocation and Scheduling (HM-MATAS), built on a Residual Heterogeneous Graph Transformer with edge and node-level attention. Our model encodes agent capabilities, travel times, and temporospatial constraints into a rich graph representation and is trainable via reinforcement learning. Trained on small-scale problems (10 agents, 20 tasks), our model generalizes effectively to significantly larger scenarios (up to 40 agents and 200 tasks), enabling fast, one-shot task assignment and scheduling. Our simultaneous model outperforms classical heuristics by assigning 164.10\% more feasible tasks given temporal constraints in 3.83\% of the time, metaheuristics by 201.54\% in 0.01\% of the time and exact solver by 231.73\% in 0.03\% of the time, while achieving $20\times$-to-$250\times$ speedup from prior graph-based methods across scales.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to improve the scalability of the solver in multi-agent task allocation and scheduling domain under constraints. The authors follow previous GNN-based methods and propose TARGETNET, which treats agents, tasks, and allocation all as nodes in a graph and relations between them as edges. In each time step, the allocation result is decided via message passing through the graph. The authors further conducted a mass of experiments against multiple baselines to show the effect of TARGETNET.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**: \n1. The presentation of the paper is very detailed. The authors have provided various materials to illustrate the methodology and the results, including graphs, equations, pseudo-code, and videos.\n2. The authors conducted extensive experiments, employing a variety of baselines and designing multiple metrics to evaluate the performances. All these lead to convincing results.\n\n**Weaknesses**:\n1. There are some expression and grammar mistakes in the paper (e.g., line 51, 163 and 181), which should be avoided in academic writing. \n2. Overall, the performance of TARGETNET is not outstanding enough compared to the baselines. It exhibits clear superiority in the computation time, but its optimal and successful rates are inferior to MILP solver in small, medium and large scale tasks. As MILP solver is supported by solid mathematical theory, users would prefer MILP in most cases, making the application of TARGETNET limited in extremely large cases.\n\nQUESTIONS:\n1. Besides the agents and tasks as nodes, the authors further include $|A||T|$ assignment nodes in the graph. When encountered with extremely large scale tasks, the complexity of the graph would increase significantly. However, the experiment results show that the computation time of TARGETNET is lower than baselines. Could the authors explain why TARGETNET maintains a high efficiency?\n\n2. As shown in Figure 3 and 4, the performance of TARGETNET is severely declined after removing the residual connections. Is this indicating that the improved performance of TARGETNET is due to the residual connections rather than the graph-based learning methodology?\n\nLIMITATIONS:\nYes but not sufficient. Please see weaknesses and questions.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes TARGETNET, a reinforcement learning (RL)-based framework for heterogeneous mobile multi-agent task allocation and scheduling problems (HM-MATAS). TARGETNET tackles HM-MATAS by formulating it as an RL problem with graph input, and then uses REINFORCE to optimize the policy. To better handle the graph input, the authors propose a new graph transformer with node and edge attention mechanisms. Along with residual connections, this graph transformer has zero-shot generalization to problems with larger scale. The proposed framework is evaluated on tasks with up to 40 agents and 200 tasks, and the results show that TARGETNET outperforms existing baselines.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The presentation of this paper is clear and well-organized.\n2. HM-MATAS is an important problem in the field of multi-agent systems and complexity theory.\n3. The proposed framework is novel and has a good performance on the benchmark tasks, especially on the larger scale tasks.\n4. This framework demonstrates how RL and GNN can be combined to effectively address large-scale NP-hard problems involving both task allocation and scheduling over graph-structured representations.\n\n\n**Weaknesses**\n1. The proposed method has a fast inference speed, while the training time is not mentioned.\n2. The interpretability of the proposed method is limited, which is a common problem in deep RL.\n3. Using RL to solve combinatorial optimization problems (e.g. graph-based problems) is not a new idea [1,2,3]. The authors should discuss more related work in this area [4,5,6].\n\n[1] Wagner, Adam Zsolt. “Constructions in combinatorics via neural networks.” ArXiv abs/2104.14516, 2021.\n\n[2] Darvariu V A, Hailes S, Musolesi M. Graph reinforcement learning for combinatorial optimization: A survey and unifying perspective[J]. arXiv preprint arXiv:2404.06492, 2024.\n\n[3] Peng Y, Choi B, Xu J. Graph learning for combinatorial optimization: a survey of state-of-the-art[J]. Data Science and Engineering, 2021.\n\n[4] Ma, Qiang, Suwen Ge, Danyang He, Darshan D. Thaker and Iddo Drori. “Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning.” ArXiv abs/1911.04936, 2019.\n\n[5] Batuhan Altundas, Zheyuan Wang, Joshua Bishop, and Matthew Gombolay. Learning Coor- dination Policies over Heterogeneous Graphs for Human-Robot Teams via Recurrent Neural Schedule Propagation.\n\n[6] Zheyuan Wang and Matthew Gombolay. Stochastic Resource Optimization over Heterogeneous Graph Neural Networks for Failure-Predictive Maintenance Scheduling.\n\nQUESTIONS:\n1. Please provide more discussion on the difference in problems and methods between this paper and [4,5,6].\n2. It is observed that the sequential decision making version of TARGETNET have better performance than the one-step version. Some prior work [7] has shown the potential to boost the performance of stateless policy by a simple distillation process, which means we can distill the sequential policy to a one-step policy by a simple behavior cloning process. Can you try this simple distillation process to boost the performance of the one-step version of TARGETNET?\n3. Can you discuss the time cost of training stage of TARGETNET?\n4. The edge attention mechanism to incorporate the edge information seems to be an important contribution of this paper. However, there is a simple way to incorporate the edge information into the node attention mechanism: we can create a new node for each edge with the edge information as the node feature. This way, we can use the same node attention mechanism to incorporate the edge information. Can you discuss the difference between this simple way and the proposed edge attention mechanism? And what if the baseline method Res-HetGAT uses this simple way to incorporate the edge information?\n\n[7] Ye, Jianing, Chenghao Li, Jianhao Wang and Chongjie Zhang. “Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework.” (2022).\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes TARGETNET, a scalable one-shot framework for simultaneous task allocation and scheduling in heterogeneous multi-agent systems under temporal constraints. By modeling the problem as a heterogeneous graph and using a Transformer with edge-aware attention, it efficiently captures agent-task relationships, travel times, and scheduling dependencies. Trained via reinforcement learning, TARGETNET generalizes from small to large-scale problems, outperforming heuristics, metaheuristics, and prior GNNs in both solution quality and inference speed.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Simultaneous task allocation and scheduling in a single forward pass — faster and more scalable than sequential or optimization-based methods.\n2. Edge-aware Heterogeneous Graph Transformer effectively captures complex agent-task relationships, including travel time, execution time, and temporal constraints.\n3. Strong generalization from small-scale training to large-scale deployment without retraining, with up to 250× speedup over baselines and near-optimal quality.\n\nWeaknesses\n1. Lack of interpretability — like most deep GNNs, the learned policy is hard to analyze or debug in safety-critical applications.\n2. Slightly weaker performance than sequential models in extremely large-scale cases, which can benefit from per-step re-optimization (but at a huge cost in time).\n3. The design of transformer seems very specific to the nature of MATAS. I wish to see a more principled design like incorperating next-token prediction, and test the scaling law, etc.\n\nQUESTIONS:\n1. There are ablation studies on TARGETNET\\ER and TARGETNET\\R, how about TARGETNET\\E?\n\n2. Why does sequential version performs better on the largest task? What are the typical failure modes of TARGETNET under this setting?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

comments: SUMMARY:
* Proposes TARGETNET, an approach which aims to enable the coordination of large teams of heterogenous mobile agents on complex tasks
* TARGETNET uses residual heterogeneous graph transformers, and its task assignment/scheduling capabilities are trained via RL

STRENGTHS AND WEAKNESSES:
Strengths
* Problem formulation is clear, and the method is a natural product of reasonable decisions
* The paper presentation is generally very polished
* The evaluation tasks seem reasonable, and the baselines are extensive
* The results are  generally strong, and the following discussion is nuanced and comprehensive

Weaknesses
* None major
* The evaluation is on fairly contrived domains, which seem somewhat removed from real-world settings; related, the authors acknowledge their approach is not directly applicable to more dynamic settings, since the plans are pre-computed
* Nitpicks
    * Figure 2a text is fairly small; would recommend ensuring fontsize in diagrams are generally at the same scale of the caption.

QUESTIONS:
* Within the MARL literature, ALMA [1] is one method that may be relevant (it also involves allocating heterogenous agents to tasks dynamically; a learning-based approach); discussion on the differences in these settings, and the comparative merits of TARGETNET and ALMA would be useful. Can TARGETNET be applied in ALMA's setting as well? Or broadly, be integrated with more general, learning-based approaches? (as I reflect, it seems that ALMA's setting is more dynamic than the one investigated here)

[1] https://arxiv.org/abs/2205.14205

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-07