arXiv - CS - Multiagent Systems最新文献_第5页

Improving the Prediction of Individual Engagement in Recommendations Using Cognitive Models 利用认知模型改进对个人参与推荐的预测

arXiv - CS - Multiagent Systems Pub Date : 2024-08-28 DOI: arxiv-2408.16147

Roderick Seow, Yunfan Zhao, Duncan Wood, Milind Tambe, Cleotilde Gonzalez

引用次数: 0

TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles TrafficGamer：利用博弈论规则为安全关键场景提供可靠灵活的交通模拟

arXiv - CS - Multiagent Systems Pub Date : 2024-08-28 DOI: arxiv-2408.15538

Guanren Qiao, Guorui Quan, Jiawei Yu, Shujun Jia, Guiliang Liu

{"title":"TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles","authors":"Guanren Qiao, Guorui Quan, Jiawei Yu, Shujun Jia, Guiliang Liu","doi":"arxiv-2408.15538","DOIUrl":"https://doi.org/arxiv-2408.15538","url":null,"abstract":"While modern Autonomous Vehicle (AV) systems can develop reliable driving\u0000policies under regular traffic conditions, they frequently struggle with\u0000safety-critical traffic scenarios. This difficulty primarily arises from the\u0000rarity of such scenarios in driving datasets and the complexities associated\u0000with predictive modeling among multiple vehicles. To support the testing and\u0000refinement of AV policies, simulating safety-critical traffic events is an\u0000essential challenge to be addressed. In this work, we introduce TrafficGamer,\u0000which facilitates game-theoretic traffic simulation by viewing common road\u0000driving as a multi-agent game. In evaluating the empirical performance across\u0000various real-world datasets, TrafficGamer ensures both fidelity and\u0000exploitability of the simulated scenarios, guaranteeing that they not only\u0000statically align with real-world traffic distribution but also efficiently\u0000capture equilibriums for representing safety-critical scenarios involving\u0000multiple agents. Additionally, the results demonstrate that TrafficGamer\u0000exhibits highly flexible simulation across various contexts. Specifically, we\u0000demonstrate that the generated scenarios can dynamically adapt to equilibriums\u0000of varying tightness by configuring risk-sensitive constraints during\u0000optimization. To the best of our knowledge, TrafficGamer is the first simulator\u0000capable of generating diverse traffic scenarios involving multiple agents. We\u0000have provided a demo webpage for the project at\u0000https://qiaoguanren.github.io/trafficgamer-demo/.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Attention Inference of Network Topology in Multi-Agent Systems 多代理系统中网络拓扑的图注意推理

arXiv - CS - Multiagent Systems Pub Date : 2024-08-27 DOI: arxiv-2408.15449

Akshay Kolli, Reza Azadeh, Kshitj Jerath

引用次数: 0

Decentralized Unlabeled Multi-agent Pathfinding Via Target And Priority Swapping (With Supplementary) 通过目标和优先级交换实现分散式无标记多代理寻路（附补充内容）

arXiv - CS - Multiagent Systems Pub Date : 2024-08-27 DOI: arxiv-2408.14948

Stepan Dergachev, Konstantin Yakovlev

{"title":"Decentralized Unlabeled Multi-agent Pathfinding Via Target And Priority Swapping (With Supplementary)","authors":"Stepan Dergachev, Konstantin Yakovlev","doi":"arxiv-2408.14948","DOIUrl":"https://doi.org/arxiv-2408.14948","url":null,"abstract":"In this paper we study a challenging variant of the multi-agent pathfinding\u0000problem (MAPF), when a set of agents must reach a set of goal locations, but it\u0000does not matter which agent reaches a specific goal - Anonymous MAPF (AMAPF).\u0000Current optimal and suboptimal AMAPF solvers rely on the existence of a\u0000centralized controller which is in charge of both target assignment and\u0000pathfinding. We extend the state of the art and present the first AMAPF solver\u0000capable of solving the problem at hand in a fully decentralized fashion, when\u0000each agent makes decisions individually and relies only on the local\u0000communication with the others. The core of our method is a priority and target\u0000swapping procedure tailored to produce consistent goal assignments (i.e. making\u0000sure that no two agents are heading towards the same goal). Coupled with an\u0000established rule-based path planning, we end up with a TP-SWAP, an efficient\u0000and flexible approach to solve decentralized AMAPF. On the theoretical side, we\u0000prove that TP-SWAP is complete (i.e. TP-SWAP guarantees that each target will\u0000be reached by some agent). Empirically, we evaluate TP-SWAP across a wide range\u0000of setups and compare it to both centralized and decentralized baselines.\u0000Indeed, TP-SWAP outperforms the fully-decentralized competitor and can even\u0000outperform the semi-decentralized one (i.e. the one relying on the initial\u0000consistent goal assignment) in terms of flowtime (a widespread cost objective\u0000in MAPF","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective 智能仓库的多代理目标分配和路径查找：多智能体深度强化学习的合作视角

arXiv - CS - Multiagent Systems Pub Date : 2024-08-25 DOI: arxiv-2408.13750

Qi Liu, Jianqi Gao, Dongjie Zhu, Xizheng Pang, Pengbin Chen, Jingxiang Guo, Yanjie Li

引用次数: 0

Reaching New Heights in Multi-Agent Collective Construction 达到多代理集体建设的新高度

arXiv - CS - Multiagent Systems Pub Date : 2024-08-24 DOI: arxiv-2408.13615

Martin Rameš, Pavel Surynek

引用次数: 0

Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning 多代理强化学习中增强多任务泛化的混合训练

arXiv - CS - Multiagent Systems Pub Date : 2024-08-24 DOI: arxiv-2408.13567

Mingliang Zhang, Sichang Su, Chengyang He, Guillaume Sartoretti

{"title":"Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning","authors":"Mingliang Zhang, Sichang Su, Chengyang He, Guillaume Sartoretti","doi":"arxiv-2408.13567","DOIUrl":"https://doi.org/arxiv-2408.13567","url":null,"abstract":"In multi-agent reinforcement learning (MARL), achieving multi-task\u0000generalization to diverse agents and objectives presents significant\u0000challenges. Existing online MARL algorithms primarily focus on single-task\u0000performance, but their lack of multi-task generalization capabilities typically\u0000results in substantial computational waste and limited real-life applicability.\u0000Meanwhile, existing offline multi-task MARL approaches are heavily dependent on\u0000data quality, often resulting in poor performance on unseen tasks. In this\u0000paper, we introduce HyGen, a novel hybrid MARL framework, Hybrid Training for\u0000Enhanced Multi-Task Generalization, which integrates online and offline\u0000learning to ensure both multi-task generalization and training efficiency.\u0000Specifically, our framework extracts potential general skills from offline\u0000multi-task datasets. We then train policies to select the optimal skills under\u0000the centralized training and decentralized execution paradigm (CTDE). During\u0000this stage, we utilize a replay buffer that integrates both offline data and\u0000online interactions. We empirically demonstrate that our framework effectively\u0000extracts and refines general skills, yielding impressive generalization to\u0000unseen tasks. Comparative analyses on the StarCraft multi-agent challenge show\u0000that HyGen outperforms a wide range of existing solely online and offline\u0000methods.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Collaboration of LLM based Agents for Finite Element Analysis 优化有限元分析中基于 LLM 的代理协作

arXiv - CS - Multiagent Systems Pub Date : 2024-08-23 DOI: arxiv-2408.13406

Chuan Tian, Yilei Zhang

引用次数: 0

MEDCO: Medical Education Copilots Based on A Multi-Agent Framework MEDCO：基于多代理框架的医学教育协同机器人

arXiv - CS - Multiagent Systems Pub Date : 2024-08-22 DOI: arxiv-2408.12496

Hao Wei, Jianing Qiu, Haibao Yu, Wu Yuan

{"title":"MEDCO: Medical Education Copilots Based on A Multi-Agent Framework","authors":"Hao Wei, Jianing Qiu, Haibao Yu, Wu Yuan","doi":"arxiv-2408.12496","DOIUrl":"https://doi.org/arxiv-2408.12496","url":null,"abstract":"Large language models (LLMs) have had a significant impact on diverse\u0000research domains, including medicine and healthcare. However, the potential of\u0000LLMs as copilots in medical education remains underexplored. Current\u0000AI-assisted educational tools are limited by their solitary learning approach\u0000and inability to simulate the multi-disciplinary and interactive nature of\u0000actual medical training. To address these limitations, we propose MEDCO\u0000(Medical EDucation COpilots), a novel multi-agent-based copilot system\u0000specially developed to emulate real-world medical training environments. MEDCO\u0000incorporates three primary agents: an agentic patient, an expert doctor, and a\u0000radiologist, facilitating a multi-modal and interactive learning environment.\u0000Our framework emphasizes the learning of proficient question-asking skills,\u0000multi-disciplinary collaboration, and peer discussions between students. Our\u0000experiments show that simulated virtual students who underwent training with\u0000MEDCO not only achieved substantial performance enhancements comparable to\u0000those of advanced models, but also demonstrated human-like learning behaviors\u0000and improvements, coupled with an increase in the number of learning samples.\u0000This work contributes to medical education by introducing a copilot that\u0000implements an interactive and collaborative learning approach. It also provides\u0000valuable insights into the effectiveness of AI-integrated training paradigms.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards 平衡法：LLM 设计的不安定强盗奖励的优先级策略

arXiv - CS - Multiagent Systems Pub Date : 2024-08-22 DOI: arxiv-2408.12112

Shresth Verma, Niclas Boehmer, Lingkai Kong, Milind Tambe

{"title":"Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards","authors":"Shresth Verma, Niclas Boehmer, Lingkai Kong, Milind Tambe","doi":"arxiv-2408.12112","DOIUrl":"https://doi.org/arxiv-2408.12112","url":null,"abstract":"LLMs are increasingly used to design reward functions based on human\u0000preferences in Reinforcement Learning (RL). We focus on LLM-designed rewards\u0000for Restless Multi-Armed Bandits, a framework for allocating limited resources\u0000among agents. In applications such as public health, this approach empowers\u0000grassroots health workers to tailor automated allocation decisions to community\u0000needs. In the presence of multiple agents, altering the reward function based\u0000on human preferences can impact subpopulations very differently, leading to\u0000complex tradeoffs and a multi-objective resource allocation problem. We are the\u0000first to present a principled method termed Social Choice Language Model for\u0000dealing with these tradeoffs for LLM-designed rewards for multiagent planners\u0000in general and restless bandits in particular. The novel part of our model is a\u0000transparent and configurable selection component, called an adjudicator,\u0000external to the LLM that controls complex tradeoffs via a user-selected social\u0000welfare function. Our experiments demonstrate that our model reliably selects\u0000more effective, aligned, and balanced reward functions compared to purely\u0000LLM-based approaches.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0