The paper proposes a centralized training with decentralized execution framework for multi‑agent UAV pathfinding in stochastic environments, leveraging multi‑agent reinforcement learning enhanced by model predictive control concepts. Agents communicate only with a centralized planner and use a distance‑weighted mean‑field observation to plan feasible paths, while multi‑step value convergence improves training efficiency. Experiments, including ablation and real‑robot studies, demonstrate the method’s effectiveness and generalization.
By Qizhen Wu, Kexin Liu, Lei Chen, Jinhu L\"u
arXiv:2607. 28679v1 Announce Type: new Abstract: Multi-agent planning problems arise in a variety of engineering applications, such as multi-robot wildfire fighting and unmanned aerial inspection in factories.
By Sheryl Paul, Vidisha Kudalkar, Anand Balakrishnan, Lars Lindemann, Alberto Speranzon, Jyotirmoy V. Deshmukh
arXiv:2606. 19632v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) enables agents to develop coordination strategies through emergent communication, but neural policies lack the formal safety guarantees required for safety-critical robotic deployment in drone swarms and autonomous vehicle fleets.
By Ahmad Farooq, Kamran Iqbal
Multi-agent reinforcement learning (MARL) enables agents to develop coordination strategies through emergent communication, but neural policies lack the formal safety guarantees required for safety-critical robotic deployment in drone swarms and autonomous vehicle fleets. We present the first end-to-end framework for safety verification of learned multi-agent communication policies through policy abstraction: neural policies are distilled into interpretable decision trees, then formally verified, with empirical validation confirming that verified safety properties transfer to original networks.
The paper presents a training-free diffusion-based motion planner that replaces learned global trajectory scores with analytical local scores derived from obstacle, smoothness, velocity, and inter-agent feasibility terms. By reconstructing trajectory scores through local interactions between neighboring waypoints and nearby constraints, the method decomposes the denoising process while preserving the optimization structure of classical trajectory methods. Experiments demonstrate that this approach generates smooth, feasible trajectories for large multi-agent tasks in complex environments quickly, outperforming learning-based and optimization baselines without requiring training data.
By Michael Y. Fatemi, Jinhao Liang, Ferdinando Fioretto
arXiv:2603.08814v2 Announce Type: replace-cross
Abstract: Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; y...
By Piyush Gupta, Sangjae Bae, Jiachen Li, David Isele
The paper proposes a method that allows agents in multi‑agent motion planning to compute with multiple prioritizations simultaneously, avoiding reliance on domain‑specific heuristics or costly iterative searches. This general approach is applied to receding‑horizon motion planning with system dynamics, and numerical experiments show near‑optimal prioritization and performance gains over state‑of‑the‑art methods with only a slight increase in computation time. Real‑time feasibility is demonstrated on a road network with ten vehicles in a Cyber‑Physical Mobility Lab.
By Patrick Scheffe, Julius Kahle, Bassam Alrifaee
arXiv:2610.25946v2 Announce Type: replace-cross
Abstract: Developing coordination policies for complex multi-agent systems relies on either labor-intensive algorithm design or opaque task-specific re...
By Matthias Eder, Pantelis Emmanouil, Vivek Farias, Fabian Fischer, Adam Jozefiak, Tianyi Peng, Thomas Ulz, Raphael Watschinger, Fangyu Wu, Andrew Zheng
arXiv:2607. 21488v1 Announce Type: cross Abstract: Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information, or rigid agent designs.
By Gil Lifshits, Igal Bilik, Gilad Katz
arXiv:2503.13415v2 Announce Type: replace-cross
Abstract: With the rapid development of artificial intelligence, intelligent decision-making techniques have gradually surpassed human levels in variou...
By Weiqiang Jin, Hongyang Du, Shixiang Tang, Biao Zhao, Guang Yang
arXiv:2606. 01478v1 Announce Type: cross Abstract: High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing the capabilities of robot algorithms.
By Martin Schuck, Marcel P. Rath, Yufei Hua, AbhisheK Goudar, SiQi Zhou, Angela P. Schoellig
The paper introduces Reinforcement Learning Enhanced LLM Agents (RLEA), a multi‑agent framework that automates the modeling of complex Vehicle Routing Problems (VRPs). RLEA employs a lightweight neural Planner trained with Soft Q‑learning to coordinate LLM‑based agents, and incorporates an evolutionary memory module and retrieval‑augmented generation to leverage experience and external solver knowledge. Experiments on 48 VRP variants show that RLEA outperforms the prior state‑of‑the‑art method, achieving a 16.67% higher success rate and significantly reducing runtime errors.
By Yi Chen, Zikang Yu, Jiahai Wang, Jinbiao Chen, Jianpeng Zhou, Zizhen Zhang