id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2b06db31bd59d4ea70ebcfe909850e0b4e4746d4343aa93de8b91e757b9839f2 | [
"arxiv",
"semantic_scholar"
] | Influence of Team Interactions on Multi-Robot Cooperation: A Relational Network Perspective | Relational networks within a team play a critical role in the performance of many real-world multi-robot systems. To successfully accomplish tasks that require cooperation and coordination, different agents (e.g., robots) necessitate different priorities based on their positioning within the team. Yet, many of the exis... | [
"Yasin Findik",
"Hamid Osooli",
"Paul Robinette",
"Kshitij Jerath",
"S. Reza Ahmadzadeh"
] | [
"cs.RO",
"cs.MA"
] | [
"Computer Science"
] | 2023-10-19T00:00:00 | https://arxiv.org/abs/2310.12910 | https://arxiv.org/pdf/2310.12910v1 | 2310.12910 | 10.1109/MRS60187.2023.10416779 | 6 | 0 | false | null | International Symposium on Multi-Robot and Multi-Agent Systems | 0.2113 |
91c6421b0fe692b2975087eb5476e0dc0d63a2c57b5bb79a427ffa63a9fc94f8 | [
"arxiv",
"semantic_scholar"
] | Collaborative Adaptation: Learning to Recover from Unforeseen Malfunctions in Multi-Robot Teams | Cooperative multi-agent reinforcement learning (MARL) approaches tackle the challenge of finding effective multi-agent cooperation strategies for accomplishing individual or shared objectives in multi-agent teams. In real-world scenarios, however, agents may encounter unforeseen failures due to constraints like battery... | [
"Yasin Findik",
"Paul Robinette",
"Kshitij Jerath",
"S. Reza Ahmadzadeh"
] | [
"cs.RO",
"cs.MA"
] | [
"Computer Science"
] | 2023-10-19T00:00:00 | https://arxiv.org/abs/2310.12909 | https://arxiv.org/pdf/2310.12909v1 | 2310.12909 | 10.48550/arXiv.2310.12909 | 3 | 0 | false | null | arXiv.org | 0.1505 |
a134ebbe69580bacf41ccaf7c8a05e0189992346b2b2bcc38343f959fb95131a | [
"arxiv",
"semantic_scholar"
] | Fact-based Agent modeling for Multi-Agent Reinforcement Learning | In multi-agent systems, agents need to interact and collaborate with other agents in environments. Agent modeling is crucial to facilitate agent interactions and make adaptive cooperation strategies. However, it is challenging for agents to model the beliefs, behaviors, and intentions of other agents in non-stationary ... | [
"Baofu Fang",
"Caiming Zheng",
"Hao Wang"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2023-10-18T00:00:00 | https://arxiv.org/abs/2310.12290 | https://arxiv.org/pdf/2310.12290v1 | 2310.12290 | 10.48550/arXiv.2310.12290 | 1 | 1 | false | null | arXiv.org | 0.1505 |
737ed4ba7b82522ce45ce407c97bb449b2d158ed82b81031f7eae6e9a6bcf7b8 | [
"arxiv",
"semantic_scholar"
] | Malicious Agent Detection for Robust Multi-Agent Collaborative Perception | Recently, multi-agent collaborative (MAC) perception has been proposed and outperformed the traditional single-agent perception in many applications, such as autonomous driving. However, MAC perception is more vulnerable to adversarial attacks than single-agent perception due to the information exchange. The attacker c... | [
"Yangheng Zhao",
"Zhen Xiang",
"Sheng Yin",
"Xianghe Pang",
"Siheng Chen",
"Yanfeng Wang"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2023-10-18T00:00:00 | https://arxiv.org/abs/2310.11901 | https://arxiv.org/pdf/2310.11901v2 | 2310.11901 | 10.48550/arXiv.2310.11901 | 12 | 1 | false | null | arXiv.org | 0.2785 |
7528c420785508d71f1e1053f27481c98ec48a3bd398a5ad46354a31a5a112be | [
"arxiv",
"semantic_scholar"
] | Balancing Autonomy and Alignment: A Multi-Dimensional Taxonomy for Autonomous LLM-powered Multi-Agent Architectures | Large language models (LLMs) have revolutionized the field of artificial intelligence, endowing it with sophisticated language understanding and generation capabilities. However, when faced with more complex and interconnected tasks that demand a profound and iterative thought process, LLMs reveal their inherent limita... | [
"Thorsten Händler"
] | [
"cs.AI",
"cs.MA",
"cs.SE"
] | [
"Computer Science"
] | 2023-10-05T00:00:00 | https://arxiv.org/abs/2310.03659 | https://arxiv.org/pdf/2310.03659v1 | 2310.03659 | 10.48550/arXiv.2310.03659 | 42 | 2 | false | null | arXiv.org | 0.4084 |
2d19bac643a69e8954aaeed51ea68bfb3cb29090a22eb1956914293c89f13929 | [
"arxiv",
"semantic_scholar"
] | LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models | Large Language Models (LLMs) have demonstrated emergent common-sense reasoning and Theory of Mind (ToM) capabilities, making them promising candidates for developing coordination agents. This study introduces the LLM-Coordination Benchmark, a novel benchmark for analyzing LLMs in the context of Pure Coordination Settin... | [
"Saaket Agashe",
"Yue Fan",
"Anthony Reyna",
"Xin Eric Wang"
] | [
"cs.CL",
"cs.MA"
] | [
"Computer Science"
] | 2023-10-05T00:00:00 | https://arxiv.org/abs/2310.03903 | https://arxiv.org/pdf/2310.03903v3 | 2310.03903 | 10.18653/v1/2025.findings-naacl.448 | 63 | 4 | true | https://github.com/eric-ai-lab/llm_coordination | North American Chapter of the Association for Computational Linguistics | 0.4515 |
3deb185969dd9bac66c4e5b99fbc8fe74fe32b4dfba58686ebd7b7d21f9b85ab | [
"arxiv",
"semantic_scholar"
] | A Game Approach to Multi-dimensional Opinion Dynamics in Social Networks with Stubborn Strategist Agents | In a social network, individuals express their opinions on several interdependent topics, and therefore the evolution of their opinions on these topics is also mutually dependent. In this work, we propose a differential game model for the multi-dimensional opinion formation of a social network whose population of agent... | [
"Hossein B. Jond",
"Aykut Yıldız"
] | [
"cs.SI"
] | [
"Computer Science"
] | 2023-10-05T00:00:00 | https://arxiv.org/abs/2310.03900 | https://arxiv.org/pdf/2310.03900v3 | 2310.03900 | 10.1016/j.ejcon.2023.100941 | 5 | 0 | false | null | European Journal of Control | 0.1945 |
47fc465c890f03bda0122731a648d5767bcd3e313b43283659e9688d65c24f8b | [
"arxiv",
"semantic_scholar"
] | Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View | As Natural Language Processing (NLP) systems are increasingly employed in intricate social environments, a pressing query emerges: Can these NLP systems mirror human-esque collaborative intelligence, in a multi-agent society consisting of multiple large language models (LLMs)? This paper probes the collaboration mechan... | [
"Jintian Zhang",
"Xin Xu",
"Ningyu Zhang",
"Ruibo Liu",
"Bryan Hooi",
"Shumin Deng"
] | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2023-10-03T00:00:00 | https://arxiv.org/abs/2310.02124 | https://arxiv.org/pdf/2310.02124v3 | 2310.02124 | 10.48550/arXiv.2310.02124 | 260 | 9 | true | https://github.com/zjunlp/MachineSoM}.} | arXiv.org | 0.6042 |
19e869fae27c0893cb8ad401984b57c38b7a53d2de40b72e5148580e44fdf19c | [
"arxiv",
"semantic_scholar"
] | Adapting LLM Agents with Universal Feedback in Communication | Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. To facilitate the training for these agents with both linguistic feedback and non-linguistic reward signals, we introduce Learning through Communication (LTC). We design a universal buffer to store all the feedback, and an itera... | [
"Kuan Wang",
"Yadong Lu",
"Michael Santacroce",
"Yeyun Gong",
"Chao Zhang",
"Yelong Shen"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2023-10-01T00:00:00 | https://arxiv.org/abs/2310.01444 | https://arxiv.org/pdf/2310.01444v3 | 2310.01444 | null | 16 | 2 | false | null | null | 0.3076 |
daf7b9e8f120a5a191621f984a786cafd39159fb2e6df63130f7fe53959c8215 | [
"arxiv",
"semantic_scholar"
] | Cooperation Dynamics in Multi-Agent Systems: Exploring Game-Theoretic Scenarios with Mean-Field Equilibria | Cooperation is fundamental in Multi-Agent Systems (MAS) and Multi-Agent Reinforcement Learning (MARL), often requiring agents to balance individual gains with collective rewards. In this regard, this paper aims to investigate strategies to invoke cooperation in game-theoretic scenarios, namely the Iterated Prisoner's D... | [
"Vaigarai Sathi",
"Sabahat Shaik",
"Jaswanth Nidamanuri"
] | [
"cs.GT",
"cs.AI"
] | [
"Computer Science"
] | 2023-09-28T00:00:00 | https://arxiv.org/abs/2309.16263 | https://arxiv.org/pdf/2309.16263v3 | 2309.16263 | 10.48550/arXiv.2309.16263 | 3 | 0 | false | null | arXiv.org | 0.1505 |
208d277e2d9ac4b4c9b532037f3725609f00fdf18ee86607a8dfad730bc2d985 | [
"arxiv",
"semantic_scholar"
] | Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem | This work presents a modular and parallelizable multi-agent deep reinforcement learning framework for imbibing cooperative as well as competitive behaviors within autonomous vehicles. We introduce AutoDRIVE Ecosystem as an enabler to develop physically accurate and graphically realistic digital twins of Nigel and F1TEN... | [
"Tanmay Vilas Samak",
"Chinmay Vilas Samak",
"Venkat Krovi"
] | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2023-09-18T00:00:00 | https://arxiv.org/abs/2309.10007 | https://arxiv.org/pdf/2309.10007v2 | 2309.10007 | 10.48550/arXiv.2309.10007 | 1 | 0 | false | null | arXiv.org | 0.0753 |
48f598240e609ea1ed01d1665f66bd1ad3bd53777861e01e6c3730d4616239b6 | [
"arxiv",
"semantic_scholar"
] | SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models | In this work, we introduce SMART-LLM, an innovative framework designed for embodied multi-robot task planning. SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models (LLMs), harnesses the power of LLMs to convert high-level task instructions provided as input into a multi-robot task plan. It accom... | [
"Shyam Sundar Kannan",
"Vishnunandan L. N. Venkatesh",
"Byung-Cheol Min"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2023-09-18T00:00:00 | https://arxiv.org/abs/2309.10062 | https://arxiv.org/pdf/2309.10062v2 | 2309.10062 | 10.1109/IROS58592.2024.10802322 | 272 | 23 | false | null | IEEE/RJS International Conference on Intelligent RObots and Systems | 0.6901 |
0aee26de3da068e08690f27651b0c6d18177e1c7c0c75650edf4998fe3c51512 | [
"arxiv",
"semantic_scholar"
] | Multi-agent Collective Construction using 3D Decomposition | This paper addresses a Multi-Agent Collective Construction (MACC) problem that aims to build a three-dimensional structure comprised of cubic blocks. We use cube-shaped robots that can carry one cubic block at a time, and move forward, reverse, left, and right to an adjacent cell of the same height or climb up and down... | [
"Akshaya Kesarimangalam Srinivasan",
"Shambhavi Singh",
"Geordan Gutow",
"Howie Choset",
"Bhaskar Vundurthy"
] | [
"cs.RO",
"cs.MA"
] | [
"Computer Science"
] | 2023-09-02T00:00:00 | https://arxiv.org/abs/2309.00985 | https://arxiv.org/pdf/2309.00985v1 | 2309.00985 | 10.1109/IROS55552.2023.10341964 | 7 | 0 | false | null | IEEE/RJS International Conference on Intelligent RObots and Systems | 0.2258 |
fb6526f44ca0729d1be6a17ede4c6b751afa59abd8aa5a7bd58a5f08f490e60e | [
"arxiv",
"semantic_scholar"
] | ZeroLeak: Using LLMs for Scalable and Cost Effective Side-Channel Patching | Security critical software, e.g., OpenSSL, comes with numerous side-channel leakages left unpatched due to a lack of resources or experts. The situation will only worsen as the pace of code development accelerates, with developers relying on Large Language Models (LLMs) to automatically generate code. In this work, we ... | [
"M. Caner Tol",
"Berk Sunar"
] | [
"cs.CR",
"cs.LG",
"cs.SE"
] | [
"Computer Science"
] | 2023-08-24T00:00:00 | https://arxiv.org/abs/2308.13062 | https://arxiv.org/pdf/2308.13062v1 | 2308.13062 | 10.48550/arXiv.2308.13062 | 8 | 0 | false | null | arXiv.org | 0.2386 |
8c40d577895ec8a8336fd6980206761fd6f1644a8ec846c1524d38c0b8464659 | [
"arxiv",
"semantic_scholar"
] | Scalable δ-Level Coherent State Synchronization of Multi-Agent Systems in the Presence of Bounded Disturbances | In this paper, we study scalable $δ-$level coherent state synchronization for multi-agent systems (MAS) where the agents are subject to bounded disturbances/noises. We propose a scale-free framework designed solely based on the knowledge of agent models and agnostic to the communication graph and the size of the networ... | [
"Donya Nojavanzadeh",
"Zhenwei Liu",
"Ali Saberi",
"Anton A. Stoorvogel"
] | [
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2023-08-23T00:00:00 | https://arxiv.org/abs/2308.11959 | https://arxiv.org/pdf/2308.11959v5 | 2308.11959 | 10.48550/arXiv.2308.11959 | 0 | 0 | false | null | arXiv.org | 0 |
86d39a648006ba7ab7973d641da17765ed0d1271126204034af9634c1d86c921 | [
"arxiv",
"semantic_scholar"
] | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, ... | [
"Weize Chen",
"Yusheng Su",
"Jingwei Zuo",
"Cheng Yang",
"Chenfei Yuan",
"Chi-Min Chan",
"Heyang Yu",
"Yaxi Lu",
"Yi-Hsin Hung",
"Chen Qian",
"Yujia Qin",
"Xin Cong",
"Ruobing Xie",
"Zhiyuan Liu",
"Maosong Sun",
"Jie Zhou"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-08-21T00:00:00 | https://arxiv.org/abs/2308.10848 | https://arxiv.org/pdf/2308.10848v3 | 2308.10848 | 10.48550/arXiv.2308.10848 | 643 | 52 | true | https://github.com/OpenBMB/AgentVerse/ | International Conference on Learning Representations | 0.8621 |
bc55dd7a1498259793b99884c947fabc4c21f836bdaa18c812ca9cbd6610e2e5 | [
"arxiv",
"semantic_scholar"
] | AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation | AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools. Using AutoGen, develop... | [
"Qingyun Wu",
"Gagan Bansal",
"Jieyu Zhang",
"Yiran Wu",
"Beibin Li",
"Erkang Zhu",
"Li Jiang",
"Xiaoyun Zhang",
"Shaokun Zhang",
"Jiale Liu",
"Ahmed Hassan Awadallah",
"Ryen W White",
"Doug Burger",
"Chi Wang"
] | [
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2023-08-16T00:00:00 | https://arxiv.org/abs/2308.08155 | https://arxiv.org/pdf/2308.08155v2 | 2308.08155 | null | 1,801 | 99 | true | null | null | 1 |
087b4ec2205c60f985c82fdcfc6a148160774fac20200dfbed58d8ce367a86fa | [
"arxiv",
"semantic_scholar"
] | ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate | Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have explored LLMs' potential as alternatives for human evaluation. While these single-agent-based approaches show promise, experimental results... | [
"Chi-Min Chan",
"Weize Chen",
"Yusheng Su",
"Jianxuan Yu",
"Wei Xue",
"Shanghang Zhang",
"Jie Fu",
"Zhiyuan Liu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-08-14T00:00:00 | https://arxiv.org/abs/2308.07201 | https://arxiv.org/pdf/2308.07201v1 | 2308.07201 | 10.48550/arXiv.2308.07201 | 939 | 80 | true | https://github.com/chanchimin/ChatEval | arXiv.org | 0.9542 |
c2db5b571e8f7b6e6eaa9f179e31ac7ed9b65c24563b995de6e1ce0c26abf80a | [
"arxiv",
"semantic_scholar"
] | MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework | Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however, are complicated through logic inconsistencies due to cascading hallu... | [
"Sirui Hong",
"Mingchen Zhuge",
"Jiaqi Chen",
"Xiawu Zheng",
"Yuheng Cheng",
"Ceyao Zhang",
"Jinlin Wang",
"Zili Wang",
"Steven Ka Shing Yau",
"Zijuan Lin",
"Liyang Zhou",
"Chenyu Ran",
"Lingfeng Xiao",
"Chenglin Wu",
"Jürgen Schmidhuber"
] | [
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2023-08-01T00:00:00 | https://arxiv.org/abs/2308.00352 | https://arxiv.org/pdf/2308.00352v7 | 2308.00352 | 10.48550/arXiv.2308.00352 | 1,925 | 150 | true | https://github.com/geekan/MetaGPT | arXiv.org | 1 |
1c1670a2704574ebb8d2a1d304d4cb5cecef53dffbd869ec7a8dcdbaf601f24c | [
"arxiv",
"semantic_scholar"
] | Using Multi-Agent MicroServices (MAMS) for Agent Based Modelling | This paper demonstrates the use of the Multi-Agent MicroServices (MAMS) architectural style through a case study based around the development of a prototype traffic simulation in which agents model a population of individuals who travel from home to work and vice versa by car. | [
"Martynas Jagutis",
"Sean Russell",
"Rem Collier"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2023-07-27T00:00:00 | https://arxiv.org/abs/2307.14745 | https://arxiv.org/pdf/2307.14745v1 | 2307.14745 | 10.48550/arXiv.2307.14745 | 1 | 0 | false | null | International Workshop on Engineering Multi-Agent Systems | 0.0753 |
7d69882f836db659386e351d657380a4e3d025bb088602ed826a8886c5610932 | [
"arxiv",
"semantic_scholar"
] | Heterogeneous Embodied Multi-Agent Collaboration | Multi-agent embodied tasks have recently been studied in complex indoor visual environments. Collaboration among multiple agents can improve work efficiency and has significant practical value. However, most of the existing research focuses on homogeneous multi-agent tasks. Compared with homogeneous agents, heterogeneo... | [
"Xinzhu Liu",
"Di Guo",
"Huaping Liu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2023-07-26T00:00:00 | https://arxiv.org/abs/2307.13957 | https://arxiv.org/pdf/2307.13957v2 | 2307.13957 | 10.1109/LRA.2024.3390588 | 18 | 0 | false | null | IEEE Robotics and Automation Letters | 0.3197 |
17d946f4bd28973520c5d87f98013efdb31b4e03a56f4ae3694f82d07ffe513b | [
"arxiv",
"semantic_scholar"
] | Deep and Decentralized Multi-Agent Coverage of a Target with Unknown Distribution | This paper proposes a new architecture for multi-agent systems to cover an unknowingly distributed fast, safely, and decentralizedly. The inter-agent communication is organized by a directed graph with fixed topology, and we model agent coordination as a decentralized leader-follower problem with time-varying communica... | [
"Hossein Rastgoftar"
] | [
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2023-07-10T00:00:00 | https://arxiv.org/abs/2307.04407 | https://arxiv.org/pdf/2307.04407v1 | 2307.04407 | 10.1109/TCNS.2025.3525802 | 4 | 0 | false | null | IEEE Transactions on Control of Network Systems | 0.1747 |
ea29edc07dc6af64bd27f4d7700d73d250bc503bedc189a558ae10e6577f31c9 | [
"arxiv",
"semantic_scholar"
] | Wireless Multi-Agent Generative AI: From Connected Intelligence to Collective Intelligence | The convergence of generative large language models (LLMs), edge networks, and multi-agent systems represents a groundbreaking synergy that holds immense promise for future wireless generations, harnessing the power of collective intelligence and paving the way for self-governed networks where intelligent decision-maki... | [
"Hang Zou",
"Qiyang Zhao",
"Lina Bariah",
"Mehdi Bennis",
"Merouane Debbah"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2023-07-06T00:00:00 | https://arxiv.org/abs/2307.02757 | https://arxiv.org/pdf/2307.02757v1 | 2307.02757 | 10.48550/arXiv.2307.02757 | 70 | 4 | false | null | arXiv.org | 0.4628 |
348bf2afaef5b1ed0d0904db9eb7eb0fb3b08b7aa4b9faf06156148d72098369 | [
"arxiv",
"semantic_scholar"
] | SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding | Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale robotic systems, where agents must plan their collision-free paths to their given goal positions. Recently, multi-agent reinforcement learning has been introduced to solve the partially observable variant of MAPF by learning a decentralized sin... | [
"Qiushi Lin",
"Hang Ma"
] | [
"cs.RO",
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2023-07-05T00:00:00 | https://arxiv.org/abs/2307.02691 | https://arxiv.org/pdf/2307.02691v1 | 2307.02691 | 10.1109/LRA.2023.3292004 | 32 | 2 | false | null | IEEE Robotics and Automation Letters | 0.3796 |
bfa3b3943866e9bbb2f6c5cfb4b291d0c4d0a5825d12976913dc5dbec511f87e | [
"arxiv",
"semantic_scholar"
] | Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment | We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent. The heterogeneous search team may only have access to a limited number of past adversary trajectories within a large search space. This problem is challenging for both model-based ... | [
"Zixuan Wu",
"Sean Ye",
"Manisha Natarajan",
"Letian Chen",
"Rohan Paleja",
"Matthew C. Gombolay"
] | [
"cs.LG",
"cs.AI",
"cs.RO"
] | [
"Computer Science"
] | 2023-06-20T00:00:00 | https://arxiv.org/abs/2306.11301 | https://arxiv.org/pdf/2306.11301v2 | 2306.11301 | 10.1109/MRS60187.2023.10416776 | 3 | 0 | false | null | International Symposium on Multi-Robot and Multi-Agent Systems | 0.1505 |
ff7a797179a51dcd1437ae561cb304b4ba9e2c4e809cd372a37bf3d1d66e83d3 | [
"arxiv",
"semantic_scholar"
] | QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction | Estimating the joint distribution of on-road agents' future trajectories is essential for autonomous driving. In this technical report, we propose a next-generation framework for joint multi-agent trajectory prediction called QCNeXt. First, we adopt the query-centric encoding paradigm for the task of joint multi-agent ... | [
"Zikang Zhou",
"Zihao Wen",
"Jianping Wang",
"Yung-Hui Li",
"Yu-Kai Huang"
] | [
"cs.CV",
"cs.RO"
] | [
"Computer Science"
] | 2023-06-18T00:00:00 | https://arxiv.org/abs/2306.10508 | https://arxiv.org/pdf/2306.10508v1 | 2306.10508 | 10.48550/arXiv.2306.10508 | 57 | 6 | false | null | arXiv.org | 0.4409 |
aede10c96d70a113f6b140168fa9e40313b1d1dc778b5d187aaf3d20a9d9f26b | [
"arxiv",
"semantic_scholar"
] | Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications | Reward design is a key component of deep reinforcement learning, yet some tasks and designer's objectives may be unnatural to define as a scalar cost function. Among the various techniques, formal methods integrated with DRL have garnered considerable attention due to their expressiveness and flexibility to define the ... | [
"Jiangwei Wang",
"Shuo Yang",
"Ziyan An",
"Songyang Han",
"Zhili Zhang",
"Rahul Mangharam",
"Meiyi Ma",
"Fei Miao"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2023-06-11T00:00:00 | https://arxiv.org/abs/2306.06808 | https://arxiv.org/pdf/2306.06808v2 | 2306.06808 | 10.1109/IROS60139.2025.11246629 | 15 | 0 | false | null | IEEE/RJS International Conference on Intelligent RObots and Systems | 0.301 |
c046152e5b522c6e2a374ee09afd6bbb58b9948f25d281d8bdb9c44f1878f80c | [
"arxiv",
"semantic_scholar"
] | Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents | Multi-Agent Reinforcement Learning (MARL) has been widely applied in many fields such as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are vulnerable to adversarial perturbations on agent states. Robustness testing for a trained model is an essential step for confirming the trustworthiness o... | [
"Ziyuan Zhou",
"Guanjun Liu"
] | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.MA"
] | [
"Computer Science"
] | 2023-06-09T00:00:00 | https://arxiv.org/abs/2306.06136 | https://arxiv.org/pdf/2306.06136v1 | 2306.06136 | 10.48550/arXiv.2306.06136 | 17 | 1 | false | null | European Conference on Artificial Intelligence | 0.3138 |
fa2a145705a2c3d8c5b44da0e4c009b156f4631c2e17bce813439de015c1bb4f | [
"arxiv",
"semantic_scholar"
] | Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents | In this paper, we present a novel framework for enhancing the capabilities of large language models (LLMs) by leveraging the power of multi-agent systems. Our framework introduces a collaborative environment where multiple intelligent agent components, each with distinctive attributes and roles, work together to handle... | [
"Yashar Talebirad",
"Amirhossein Nadiri"
] | [
"cs.AI",
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2023-06-05T00:00:00 | https://arxiv.org/abs/2306.03314 | https://arxiv.org/pdf/2306.03314v1 | 2306.03314 | 10.48550/arXiv.2306.03314 | 438 | 13 | false | null | arXiv.org | 0.6606 |
71f3d26cc759e1398a412cf29ef1bb5b8063ad2373b1868c77bace9577b23e8e | [
"arxiv",
"semantic_scholar"
] | A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning | In fully cooperative multi-agent reinforcement learning (MARL) settings, environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of other agents. To address the above issues, we proposed a unified framework, called DFAC, for integrating distributional RL ... | [
"Wei-Fang Sun",
"Cheng-Kuang Lee",
"Simon See",
"Chun-Yi Lee"
] | [
"cs.MA",
"cs.LG"
] | [
"Computer Science"
] | 2023-06-04T00:00:00 | https://arxiv.org/abs/2306.02430 | https://arxiv.org/pdf/2306.02430v1 | 2306.02430 | 10.48550/arXiv.2306.02430 | 3 | 0 | false | null | Journal of machine learning research | 0.1505 |
bb277ce53e5624d5a94a8998a568f44e8ea4abde39887fafcc00ff730ec9f674 | [
"arxiv",
"semantic_scholar"
] | Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning | Fairness plays a crucial role in various multi-agent systems (e.g., communication networks, financial markets, etc.). Many multi-agent dynamical interactions can be cast as Markov Decision Processes (MDPs). While existing research has focused on studying fairness in known environments, the exploration of fairness in su... | [
"Peizhong Ju",
"Arnob Ghosh",
"Ness B. Shroff"
] | [
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2023-06-01T00:00:00 | https://arxiv.org/abs/2306.00324 | https://arxiv.org/pdf/2306.00324v1 | 2306.00324 | 10.48550/arXiv.2306.00324 | 8 | 0 | false | null | arXiv.org | 0.2386 |
92660930e7c78cb92dafd276a0aaf4aa660091f9c6e944b027c5d91744326843 | [
"arxiv",
"semantic_scholar"
] | Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits | The study of collaborative multi-agent bandits has attracted significant attention recently. In light of this, we initiate the study of a new collaborative setting, consisting of $N$ agents such that each agent is learning one of $M$ stochastic multi-armed bandits to minimize their group cumulative regret. We develop d... | [
"Ronshee Chawla",
"Daniel Vial",
"Sanjay Shakkottai",
"R. Srikant"
] | [
"cs.LG",
"cs.DC",
"cs.MA",
"cs.SI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2023-05-30T00:00:00 | https://arxiv.org/abs/2305.18784 | https://arxiv.org/pdf/2305.18784v2 | 2305.18784 | 10.48550/arXiv.2305.18784 | 8 | 1 | false | null | International Conference on Machine Learning | 0.2386 |
948c1b5c4a8828a8050c86b87d69d2b0699132477da8532e967df17a7b2a28ec | [
"arxiv",
"semantic_scholar"
] | Collaborative Multi-Agent Video Fast-Forwarding | Multi-agent applications have recently gained significant popularity. In many computer vision tasks, a network of agents, such as a team of robots with cameras, could work collaboratively to perceive the environment for efficient and accurate situation awareness. However, these agents often have limited computation, co... | [
"Shuyue Lan",
"Zhilu Wang",
"Ermin Wei",
"Amit K. Roy-Chowdhury",
"Qi Zhu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2023-05-27T00:00:00 | https://arxiv.org/abs/2305.17569 | https://arxiv.org/pdf/2305.17569v1 | 2305.17569 | 10.1109/TMM.2023.3275853 | 6 | 0 | false | null | IEEE transactions on multimedia | 0.2113 |
f7bf76332e0f5e61f65bc9fdd3eb59ec0659831b8626fb25309e107564d01936 | [
"arxiv",
"semantic_scholar"
] | Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning | Policy optimization methods with function approximation are widely used in multi-agent reinforcement learning. However, it remains elusive how to design such algorithms with statistical guarantees. Leveraging a multi-agent performance difference lemma that characterizes the landscape of multi-agent policy optimization,... | [
"Yulai Zhao",
"Zhuoran Yang",
"Zhaoran Wang",
"Jason D. Lee"
] | [
"cs.LG",
"cs.GT",
"cs.MA",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2023-05-08T00:00:00 | https://arxiv.org/abs/2305.04819 | https://arxiv.org/pdf/2305.04819v1 | 2305.04819 | 10.48550/arXiv.2305.04819 | 9 | 1 | false | null | International Conference on Machine Learning | 0.25 |
f2a41bdc20e857ba7315294a5ed8f737faec48d9461ab5f5c79603617ed5cd40 | [
"arxiv",
"semantic_scholar"
] | Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments | Motion planning of autonomous agents in partially known environments with incomplete information is a challenging problem, particularly for complex tasks. This paper proposes a model-free reinforcement learning approach to address this problem. We formulate motion planning as a probabilistic-labeled partially observabl... | [
"Junchao Li",
"Mingyu Cai",
"Zhen Kan",
"Shaoping Xiao"
] | [
"cs.AI",
"cs.FL",
"cs.MA",
"cs.RO",
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2023-04-30T00:00:00 | https://arxiv.org/abs/2305.00561 | https://arxiv.org/pdf/2305.00561v1 | 2305.00561 | 10.48550/arXiv.2305.00561 | 2 | 0 | false | null | arXiv.org | 0.1193 |
2730c2392a766064ac8415fefeae13cac9133ec42919cd7980fcff86d67a479f | [
"arxiv",
"semantic_scholar"
] | Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention | Traditional multi-agent reinforcement learning algorithms are difficultly applied in a large-scale multi-agent environment. The introduction of mean field theory has enhanced the scalability of multi-agent reinforcement learning in recent years. This paper considers partially observable multi-agent reinforcement learni... | [
"Min Yang",
"Guanjun Liu",
"Ziyuan Zhou"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2023-04-25T00:00:00 | https://arxiv.org/abs/2304.12653 | https://arxiv.org/pdf/2304.12653v4 | 2304.12653 | 10.3390/drones7070476 | 24 | 0 | true | https://github.com/yangmin32/GPMF} | Drones | 0.3495 |
13b94c9db946217a2a8c9e3ca4a443fd1a590ec1fc75a2db3631604b7875520c | [
"arxiv",
"semantic_scholar"
] | Multi-agent Policy Reciprocity with Theoretical Guarantee | Modern multi-agent reinforcement learning (RL) algorithms hold great potential for solving a variety of real-world problems. However, they do not fully exploit cross-agent knowledge to reduce sample complexity and improve performance. Although transfer RL supports knowledge sharing, it is hyperparameter sensitive and c... | [
"Haozhi Wang",
"Yinchuan Li",
"Qing Wang",
"Yunfeng Shao",
"Jianye Hao"
] | [
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2023-04-12T00:00:00 | https://arxiv.org/abs/2304.05632 | https://arxiv.org/pdf/2304.05632v1 | 2304.05632 | 10.48550/arXiv.2304.05632 | 1 | 0 | false | null | arXiv.org | 0.0753 |
1d4a21a3bc6317d3655a6565ad40882017a4ea556cf0f7c39e0aaae9d6b24f57 | [
"arxiv",
"semantic_scholar"
] | Adaptive parallelization of multi-agent simulations with localized dynamics | Agent-based modelling constitutes a versatile approach to representing and simulating complex systems. Studying large-scale systems is challenging because of the computational time required for the simulation runs: scaling is at least linear in system size (number of agents). Given the inherently modular nature of MABS... | [
"Alexandru-Ionuţ Băbeanu",
"Tatiana Filatova",
"Jan H. Kwakkel",
"Neil Yorke-Smith"
] | [
"cs.DC",
"cs.CE",
"cs.MA",
"physics.soc-ph"
] | [
"Computer Science",
"Physics"
] | 2023-04-04T00:00:00 | https://arxiv.org/abs/2304.01724 | https://arxiv.org/pdf/2304.01724v1 | 2304.01724 | 10.48550/arXiv.2304.01724 | 2 | 0 | false | null | arXiv.org | 0.1193 |
5da36cc2c3c5f46f18b8b61195df2d3da1219b6f3c1c69923042ceab48ba9400 | [
"arxiv",
"semantic_scholar"
] | Attrition-Aware Adaptation for Multi-Agent Patrolling | Multi-agent patrolling is a key problem in a variety of domains such as intrusion detection, area surveillance, and policing which involves repeated visits by a group of agents to specified points in an environment. While the problem is well-studied, most works do not provide performance guarantees and either do not co... | [
"Anthony Goeckner",
"Xinliang Li",
"Ermin Wei",
"Qi Zhu"
] | [
"cs.MA",
"cs.RO"
] | [
"Computer Science"
] | 2023-04-03T00:00:00 | https://arxiv.org/abs/2304.01386 | https://arxiv.org/pdf/2304.01386v3 | 2304.01386 | 10.1109/LRA.2024.3421793 | 8 | 1 | false | null | IEEE Robotics and Automation Letters | 0.2386 |
184b23f10c3b44ef608b715fb581b7148e967113b925dd57394a442095ad811b | [
"arxiv",
"semantic_scholar"
] | A Hierarchical Game-Theoretic Decision-Making for Cooperative Multi-Agent Systems Under the Presence of Adversarial Agents | Underlying relationships among Multi-Agent Systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. This paper proposes a new hierarchical network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS d... | [
"Qin Yang",
"Ramviyas Parasuraman"
] | [
"cs.MA",
"cs.AI",
"cs.LG",
"cs.RO",
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2023-03-28T00:00:00 | https://arxiv.org/abs/2303.16641 | https://arxiv.org/pdf/2303.16641v1 | 2303.16641 | 10.1145/3555776.3577642 | 8 | 0 | false | null | ACM Symposium on Applied Computing | 0.2386 |
7acf53b8be42a7274ef35e510b12a8f269afae8c593ebbb72f82de18729bb233 | [
"arxiv",
"semantic_scholar"
] | Projected Multi-Agent Consensus Equilibrium (PMACE) with Application to Ptychography | Multi-Agent Consensus Equilibrium (MACE) formulates an inverse imaging problem as a balance among multiple update agents such as data-fitting terms and denoisers. However, each such agent operates on a separate copy of the full image, leading to redundant memory use and slow convergence when each agent affects only a s... | [
"Qiuchen Zhai",
"Gregery T. Buzzard",
"Kevin Mertes",
"Brendt Wohlberg",
"Charles A. Bouman"
] | [
"math.OC",
"eess.IV"
] | [
"Computer Science",
"Mathematics",
"Engineering"
] | 2023-03-28T00:00:00 | https://arxiv.org/abs/2303.15679 | https://arxiv.org/pdf/2303.15679v2 | 2303.15679 | 10.1109/TCI.2023.3328288 | 8 | 0 | false | null | IEEE Transactions on Computational Imaging | 0.2386 |
25beb45b3864434f9b7e79acd9b9edff2f4619766ec08613a67a935db993c7fe | [
"arxiv",
"semantic_scholar"
] | Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers | Bayesian optimization (BO) is a powerful black-box optimization framework that looks to efficiently learn the global optimum of an unknown system by systematically trading-off between exploration and exploitation. However, the use of BO as a tool for coordinated decision-making in multi-agent systems with unknown struc... | [
"Dinesh Krishnamoorthy",
"Joel A. Paulson"
] | [
"math.OC",
"cs.DC",
"cs.MA",
"eess.SY"
] | [
"Mathematics",
"Computer Science",
"Engineering"
] | 2023-03-25T00:00:00 | https://arxiv.org/abs/2303.14414 | https://arxiv.org/pdf/2303.14414v1 | 2303.14414 | 10.48550/arXiv.2303.14414 | 8 | 2 | false | null | IFAC-PapersOnLine | 0.2386 |
05750e536ca90f918e0368a143195bf5f4aaec0f8158f2474f7d84e0382d53f1 | [
"arxiv",
"semantic_scholar"
] | Learning Reward Machines in Cooperative Multi-Agent Tasks | This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) that combines cooperative task decomposition with the learning of reward machines (RMs) encoding the structure of the sub-tasks. The proposed method helps deal with the non-Markovian nature of the rewards in partially observable environme... | [
"Leo Ardon",
"Daniel Furelos-Blanco",
"Alessandra Russo"
] | [
"cs.AI",
"cs.MA",
"cs.SC"
] | [
"Computer Science"
] | 2023-03-24T00:00:00 | https://arxiv.org/abs/2303.14061 | https://arxiv.org/pdf/2303.14061v4 | 2303.14061 | 10.1007/978-3-031-56255-6_3 | 8 | 0 | false | null | null | 0.2386 |
7e0bbf9f77918044313c3b6d39fc33b98d8ca629a73a44446d025c4a88a840fb | [
"arxiv",
"semantic_scholar"
] | Coordinating Fully-Cooperative Agents Using Hierarchical Learning Anticipation | Learning anticipation is a reasoning paradigm in multi-agent reinforcement learning, where agents, during learning, consider the anticipated learning of other agents. There has been substantial research into the role of learning anticipation in improving cooperation among self-interested agents in general-sum games. Tw... | [
"Ariyan Bighashdel",
"Daan de Geus",
"Pavol Jancura",
"Gijs Dubbelman"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2023-03-15T00:00:00 | https://arxiv.org/abs/2303.08307 | https://arxiv.org/pdf/2303.08307v2 | 2303.08307 | 10.48550/arXiv.2303.08307 | 2 | 0 | false | null | arXiv.org | 0.1193 |
26fa1b2e8c71d10601097d31c3f3503ddcb86996fe34950c79aa1c4cc96a84ee | [
"arxiv",
"semantic_scholar"
] | Online Control Barrier Functions for Decentralized Multi-Agent Navigation | Control barrier functions (CBFs) enable guaranteed safe multi-agent navigation in the continuous domain. The resulting navigation performance, however, is highly sensitive to the underlying hyperparameters. Traditional approaches consider fixed CBFs (where parameters are tuned apriori), and hence, typically do not perf... | [
"Zhan Gao",
"Guang Yang",
"Amanda Prorok"
] | [
"cs.RO",
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2023-03-08T00:00:00 | https://arxiv.org/abs/2303.04313 | https://arxiv.org/pdf/2303.04313v2 | 2303.04313 | 10.1109/MRS60187.2023.10416796 | 28 | 1 | false | null | International Symposium on Multi-Robot and Multi-Agent Systems | 0.3656 |
915fc6329948e021f2b9843da1904357dd6623b21190f4e9b805065aa35a17fe | [
"arxiv",
"semantic_scholar"
] | Coordination of Multiple Robots along Given Paths with Bounded Junction Complexity | We study a fundamental NP-hard motion coordination problem for multi-robot/multi-agent systems: We are given a graph $G$ and set of agents, where each agent has a given directed path in $G$. Each agent is initially located on the first vertex of its path. At each time step an agent can move to the next vertex on its pa... | [
"Mikkel Abrahamsen",
"Tzvika Geft",
"Dan Halperin",
"Barak Ugav"
] | [
"cs.RO",
"cs.CG",
"cs.DS",
"cs.MA"
] | [
"Computer Science"
] | 2023-03-01T00:00:00 | https://arxiv.org/abs/2303.00745 | https://arxiv.org/pdf/2303.00745v1 | 2303.00745 | 10.48550/arXiv.2303.00745 | 6 | 1 | false | null | Adaptive Agents and Multi-Agent Systems | 0.2113 |
ba338771136c539d9423a2eda5bf50e6bb77d9277192a11a1a1b05353ccba2fb | [
"arxiv",
"semantic_scholar"
] | Causal Explanations for Sequential Decision-Making in Multi-Agent Systems | We present CEMA: Causal Explanations in Multi-Agent systems; a framework for creating causal natural language explanations of an agent's decisions in dynamic sequential multi-agent systems to build more trustworthy autonomous agents. Unlike prior work that assumes a fixed causal structure, CEMA only requires a probabil... | [
"Balint Gyevnar",
"Cheng Wang",
"Christopher G. Lucas",
"Shay B. Cohen",
"Stefano V. Albrecht"
] | [
"cs.AI",
"cs.RO"
] | [
"Computer Science"
] | 2023-02-21T00:00:00 | https://arxiv.org/abs/2302.10809 | https://arxiv.org/pdf/2302.10809v4 | 2302.10809 | 10.5555/3635637.3662930 | 17 | 0 | false | null | Adaptive Agents and Multi-Agent Systems | 0.3138 |
8454ab4622bd6879f8a1ca8d2d5d401031aaeaf0593348355fdf0be9fbdc7823 | [
"arxiv",
"semantic_scholar"
] | MANSA: Learning Fast and Slow in Multi-Agent Systems | In multi-agent reinforcement learning (MARL), independent learning (IL) often shows remarkable performance and easily scales with the number of agents. Yet, using IL can be inefficient and runs the risk of failing to successfully train, particularly in scenarios that require agents to coordinate their actions. Using ce... | [
"David Mguni",
"Haojun Chen",
"Taher Jafferjee",
"Jianhong Wang",
"Long Fei",
"Xidong Feng",
"Stephen McAleer",
"Feifei Tong",
"Jun Wang",
"Yaodong Yang"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2023-02-12T00:00:00 | https://arxiv.org/abs/2302.05910 | https://arxiv.org/pdf/2302.05910v3 | 2302.05910 | 10.48550/arXiv.2302.05910 | 4 | 0 | false | null | International Conference on Machine Learning | 0.1747 |
2f3223fd40829153a60a2a149ddd568e15936126ef773b8fde9a9d5afab36c53 | [
"arxiv",
"semantic_scholar"
] | Learning cooperative behaviours in adversarial multi-agent systems | This work extends an existing virtual multi-agent platform called RoboSumo to create TripleSumo -- a platform for investigating multi-agent cooperative behaviors in continuous action spaces, with physical contact in an adversarial environment. In this paper we investigate a scenario in which two agents, namely `Bug' an... | [
"Ni Wang",
"Gautham P. Das",
"Alan G. Millard"
] | [
"cs.AI",
"cs.MA",
"cs.RO"
] | [
"Computer Science"
] | 2023-02-10T00:00:00 | https://arxiv.org/abs/2302.05528 | https://arxiv.org/pdf/2302.05528v1 | 2302.05528 | 10.1007/978-3-031-15908-4_15 | 0 | 0 | false | null | Towards Autonomous Robotic Systems | 0 |
6da096c39005dedb3c406251fec7ffe1fa09f53456a6b7800a6fab3b981627a3 | [
"arxiv",
"semantic_scholar"
] | Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation | This paper investigates the multi-agent navigation problem, which requires multiple agents to reach the target goals in a limited time. Multi-agent reinforcement learning (MARL) has shown promising results for solving this issue. However, it is inefficient for MARL to directly explore the (nearly) optimal policy in the... | [
"Xinyi Yang",
"Shiyu Huang",
"Yiwen Sun",
"Yuxiang Yang",
"Chao Yu",
"Wei-Wei Tu",
"Huazhong Yang",
"Yu Wang"
] | [
"cs.RO",
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2023-02-08T00:00:00 | https://arxiv.org/abs/2302.04094 | https://arxiv.org/pdf/2302.04094v1 | 2302.04094 | 10.48550/arXiv.2302.04094 | 13 | 3 | false | null | Adaptive Agents and Multi-Agent Systems | 0.301 |
667b501df0ae21307846c6c9c216a2dfe148d450cff45783b9030bbb442344af | [
"arxiv",
"semantic_scholar"
] | Uncoupled Learning of Differential Stackelberg Equilibria with Commitments | In multi-agent problems requiring a high degree of cooperation, success often depends on the ability of the agents to adapt to each other's behavior. A natural solution concept in such settings is the Stackelberg equilibrium, in which the ``leader'' agent selects the strategy that maximizes its own payoff given that th... | [
"Robert Loftin",
"Mustafa Mert Çelikok",
"Herke van Hoof",
"Samuel Kaski",
"Frans A. Oliehoek"
] | [
"cs.LG",
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2023-02-07T00:00:00 | https://arxiv.org/abs/2302.03438 | https://arxiv.org/pdf/2302.03438v2 | 2302.03438 | 10.5555/3635637.3662984 | 2 | 0 | false | null | Adaptive Agents and Multi-Agent Systems | 0.1193 |
22174c264b96bc4483b4fca287bb0d2b2b8cd8a331db1c9c367078e62467c446 | [
"arxiv",
"semantic_scholar"
] | Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning | Multi-agent reinforcement learning (MARL) requires agents to explore within a vast joint action space to find joint actions that lead to coordination. Existing value-based MARL algorithms commonly rely on random exploration, such as $ε$-greedy, to explore the environment which is not systematic and inefficient at ident... | [
"Lukas Schäfer",
"Oliver Slumbers",
"Stephen McAleer",
"Yali Du",
"Stefano V. Albrecht",
"David Mguni"
] | [
"cs.MA",
"cs.LG"
] | [
"Computer Science"
] | 2023-02-07T00:00:00 | https://arxiv.org/abs/2302.03439 | https://arxiv.org/pdf/2302.03439v7 | 2302.03439 | 10.48550/arXiv.2302.03439 | 10 | 0 | false | null | Adaptive Agents and Multi-Agent Systems | 0.2603 |
6c7edca278dc978c0ee44dcb63315a3e82417849f1f63394dbf60b533c5cb07e | [
"arxiv",
"semantic_scholar"
] | Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning | Being able to harness the power of large datasets for developing cooperative multi-agent controllers promises to unlock enormous value for real-world applications. Many important industrial systems are multi-agent in nature and are difficult to model using bespoke simulators. However, in industry, distributed processes... | [
"Claude Formanek",
"Asad Jeewa",
"Jonathan Shock",
"Arnu Pretorius"
] | [
"cs.LG",
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2023-02-01T00:00:00 | https://arxiv.org/abs/2302.00521 | https://arxiv.org/pdf/2302.00521v2 | 2302.00521 | null | 8 | 0 | false | null | null | 0.2386 |
85f333a655c0610b240094eb962574d9a0669d88a4c1b1ef8ce58672adb0ed0e | [
"arxiv",
"semantic_scholar"
] | Multi-Agent Contract Design: How to Commission Multiple Agents with Individual Outcome | We study hidden-action principal-agent problems with multiple agents. These are problems in which a principal commits to an outcome-dependent payment scheme in order to incentivize some agents to take costly, unobservable actions that lead to favorable outcomes. Previous works on multi-agent problems study models where... | [
"Matteo Castiglioni",
"Alberto Marchesi",
"Nicola Gatti"
] | [
"cs.GT"
] | [
"Computer Science"
] | 2023-01-31T00:00:00 | https://arxiv.org/abs/2301.13654 | https://arxiv.org/pdf/2301.13654v1 | 2301.13654 | 10.1145/3580507.3597793 | 38 | 1 | false | null | ACM Conference on Economics and Computation | 0.3978 |
90a4b0f6ea6f2b7a4c1fae1ac304e51c263abdec892917251801462b1fffe699 | [
"arxiv",
"semantic_scholar"
] | Multi-Agent Congestion Cost Minimization With Linear Function Approximations | This work considers multiple agents traversing a network from a source node to the goal node. The cost to an agent for traveling a link has a private as well as a congestion component. The agent's objective is to find a path to the goal node with minimum overall cost in a decentralized way. We model this as a fully dec... | [
"Prashant Trivedi",
"Nandyala Hemachandra"
] | [
"cs.LG",
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2023-01-26T00:00:00 | https://arxiv.org/abs/2301.10993 | https://arxiv.org/pdf/2301.10993v2 | 2301.10993 | 10.48550/arXiv.2301.10993 | 2 | 0 | false | null | International Conference on Artificial Intelligence and Statistics | 0.1193 |
fdb973443104861e8de3e5b74aa2ab4505e5cdde5c84c8908f50275041cd8fe2 | [
"arxiv",
"semantic_scholar"
] | Periodic Multi-Agent Path Planning | Multi-agent path planning (MAPP) is the problem of planning collision-free trajectories from start to goal locations for a team of agents. This work explores a relatively unexplored setting of MAPP where streams of agents have to go through the starts and goals with high throughput. We tackle this problem by formulatin... | [
"Kazumi Kasaura",
"Ryo Yonetani",
"Mai Nishimura"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2023-01-26T00:00:00 | https://arxiv.org/abs/2301.10910 | https://arxiv.org/pdf/2301.10910v2 | 2301.10910 | 10.1609/aaai.v37i5.25762 | 4 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.1747 |
82be53695dd8f057fd755d0aa83a30ea1de1e2d319ee042a3635f4ef76ba52c3 | [
"arxiv",
"semantic_scholar"
] | Decentralized Multi-agent Filtering | This paper addresses the considerations that comes along with adopting decentralized communication for multi-agent localization applications in discrete state spaces. In this framework, we extend the original formulation of the Bayes filter, a foundational probabilistic tool for discrete state estimation, by appending ... | [
"Dom Huh",
"Prasant Mohapatra"
] | [
"cs.MA",
"cs.AI"
] | [
"Computer Science"
] | 2023-01-21T00:00:00 | https://arxiv.org/abs/2301.08864 | https://arxiv.org/pdf/2301.08864v1 | 2301.08864 | 10.48550/arXiv.2301.08864 | 0 | 0 | false | null | arXiv.org | 0 |
ad02fef0c1a6198bdd5a378cbcd1d72bb2b6b12f53a1642764fabedb4a72fe44 | [
"arxiv",
"semantic_scholar"
] | Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems | Solving the problem of cooperation is fundamentally important for the creation and maintenance of functional societies. Problems of cooperation are omnipresent within human society, with examples ranging from navigating busy road junctions to negotiating treaties. As the use of AI becomes more pervasive throughout soci... | [
"Nayana Dasgupta",
"Mirco Musolesi"
] | [
"cs.MA",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2023-01-19T00:00:00 | https://arxiv.org/abs/2301.08278 | https://arxiv.org/pdf/2301.08278v3 | 2301.08278 | 10.1007/s10458-025-09698-5 | 12 | 0 | false | null | Autonomous Agents and Multi-Agent Systems | 0.2785 |
dd310da5f8ed205e8184b20263d109b4614ee5a056fdb6cd26480bafa4095fea | [
"arxiv",
"semantic_scholar"
] | Multi-Agent Coordination Fluid Flow Modeling and Experimental Evaluation | Reliability is a critical aspect of multi-agent system coordination as it ensures that the system functions correctly and consistently. If one agent in the system fails or behaves unexpectedly, it can negatively impact the performance and effectiveness of the entire system. Therefore, it is important to design and impl... | [
"Harshvardhan Uppaluru",
"Mohammad Ghuran",
"Hossein Rastgoftar"
] | [
"eess.SY"
] | [
"Engineering",
"Computer Science"
] | 2023-01-14T00:00:00 | https://arxiv.org/abs/2301.05833 | https://arxiv.org/pdf/2301.05833v3 | 2301.05833 | null | 1 | 0 | false | null | null | 0.0753 |
84933c2328030669510ca0cac34782acff329f4a1ab57792f893fcef55b611bf | [
"arxiv",
"semantic_scholar"
] | Self-Motivated Multi-Agent Exploration | In cooperative multi-agent reinforcement learning (CMARL), it is critical for agents to achieve a balance between self-exploration and team collaboration. However, agents can hardly accomplish the team task without coordination and they would be trapped in a local optimum where easy cooperation is accessed without enou... | [
"Shaowei Zhang",
"Jiahan Cao",
"Lei Yuan",
"Yang Yu",
"De-Chuan Zhan"
] | [
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2023-01-05T00:00:00 | https://arxiv.org/abs/2301.02083 | https://arxiv.org/pdf/2301.02083v2 | 2301.02083 | 10.48550/arXiv.2301.02083 | 11 | 1 | false | null | Adaptive Agents and Multi-Agent Systems | 0.2698 |
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