The paper introduces Hierarchical Reinforcement and Collective Learning (HRCL), a framework that combines multi‑agent reinforcement learning (MARL) with decentralized coordination. HRCL uses MARL at a high level to generate strategic guidance that limits the decision space for low‑level agents, enabling efficient short‑term coordination while considering long‑term effects. Experiments on synthetic, energy‑management, and drone‑swarm scenarios demonstrate faster convergence and significant reductions in system‑wide and individual costs compared to standalone MARL.
By Chuhao Qin, Evangelos Pournaras
AeroWeaver is a new embodied‑agent harness that integrates large language model (LLM) decision making with the executable skills of individual UAVs, enabling distributed, adaptive swarm execution. It connects semantic mission decisions to governed skills, organizes role‑conditioned local agents for coordination, and refines skill selection online using role‑indexed state‑action‑reward experience. Experiments demonstrate that AeroWeaver maintains valid skill execution without a central joint‑action generator and supports reward‑guided, training‑free adaptive learning from accumulated execution experience.
By Jiabin Lou, Yirong Yang, Haopeng Wang, Xuxin Lv, Xinyu Liu, Diyuan Hou, Xuehong Liu, Rongye Shi, Wenjun Wu
arXiv:2609.40137v1 Announce Type: cross
Abstract: We present Game-Guided Skill Discovery (GGSD), a framework that uses self-play in games to discover motor skills that are directly playable by humans...
By Seungeun Rho, Jeonghwan Kim, Xue Bin Peng, Sehoon Ha
The paper introduces COMPASS, a decentralized architecture that uses spatial transformers to generate local feedback tokens for large collectives of AI agents in robotics. By aggregating multi‑hop messages, COMPASS enables scalable control of up to 1024 robots, achieving cohesive flocking formations and accurate execution of natural language commands. Experiments show that structured diversity in input commands improves performance and that learned feedback tokens outperform hand‑crafted raw state feedback.
By Frederic Vatnsdal, Roshan Gopal, Romina Garcia Camargo, Vijay Kumar, Alejandro Ribeiro
The paper introduces the Shared Recurrent Memory Transformer (SRMT), a decentralized multi‑agent reinforcement learning framework that uses a global memory workspace for agents to broadcast and query each other’s learned states. SRMT is evaluated on the Partially Observable Multi‑Agent Pathfinding (PO‑MAPF) problem, showing that shared memory enables emergent coordination even with minimal reward guidance and outperforms existing baselines on the Bottleneck task and scales competitively on larger POGEMA maps. The authors provide open‑source code for training and evaluation on GitHub.
By Alsu Sagirova, Yuri Kuratov, Mikhail Burtsev
arXiv:2607. 12861v1 Announce Type: cross Abstract: Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications.
By Yize Mi, Jianan Li, Liang Li, Shiyu Zhao
arXiv:2608. 15372v1 Announce Type: new Abstract: We study generating game-theoretically optimized Courses of Action (COAs) for a Blue UAS swarm against an adaptive Red adversary in a communication-degraded environment, motivated by (but not derived from) a public U.
By Phillip Jiang
arXiv:2606. 12352v1 Announce Type: cross Abstract: Multi-robot collaboration allows robots to efficiently take on a wide range of tasks, from moving a couch through a doorway to assembling structures on a construction site.
By Ria Doshi, Tian Gao, Annie Chen, Chelsea Finn, Jeannette Bohg
The paper demonstrates that a minimal foraging swarm can approach the explore/exploit optimum of reinforcement learning without any learner or reward signal. Agents use heavy‑tailed Levy flights to explore, recruit neighbors via a threshold cascade on their proximity network, and disperse as patch crowds, creating a cycle that balances exploration and exploitation. The swarm attains the shape of the ideal‑free distribution (committed foragers track richness with slope 0.94) and reaches this allocation dynamically in about 150 steps, though it remains slightly exploitable and does not achieve the exact equilibrium.
By Galen J. Wilkerson
Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications. Furthermore, complex swarm behaviors can surprisingly emerge from simple rewards without explicit aggregation incentives.
Collective intelligence is a collaborative autonomy paradigm in which multiple agents pursue shared objectives through local perception, information exchange, and coordinated action. UAV swarms embody...
arXiv:2610.07556v1 Announce Type: new
Abstract: Learned orchestration can automatically construct effective language-model multi-agent systems, but existing approaches couple planning to fixed worker...
By Xinle Wu, Yao Lu