Multi-Agent Reinforcement Learning · 概念图谱

引用本概念的论文(24)

HiComm:面向多智能体强化学习的分层通信 HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning arXiv 2606.29126
HyPOLE：超属性引导的部分可观测多智能体强化学习 HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation arXiv 2606.30966
MUTE：面向高效多智能体协同的回报保持型通信遗忘方法 MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination arXiv 2607.03473
PRIME：面向无人机辅助应急通信网络的多智能体环境可塑性恢复 PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks arXiv 2607.17922
基于多智能体深度强化学习与Petri网的柔性制造系统动态调度 Dynamic Scheduling for Flexible Manufacturing Systems Based on Multi-Agent Deep Reinforcement Learning and Petri Nets arXiv 2606.31737
多智能体强化学习控制 Rayleigh-Bénard 对流中的稀疏传感器布置 Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection arXiv 2606.30238
大规模电动汽车车队的智能充电：独立多智能体强化学习方法 Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches arXiv 2606.31347
面向高速铁路市场动态定价的关系型多智能体强化学习 Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets arXiv 2607.05179
FootsiesGym：一种面向双人零和不完美信息博弈的格斗游戏基准 FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games arXiv 2607.06514
基于多智能体深度强化学习的奶牛场多目标电池管理 Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms arXiv 2607.06489
基于采样的协调感知多目标多机器人强化学习 Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning arXiv 2606.30893
面向无人机移动边缘计算中 SLA 感知网络切片的多智能体强化学习 Multi-Agent Reinforcement Learning for SLA-Aware Network Slicing in UAV-Enabled MEC arXiv 2607.09295
COLMAR:面向多智能体主动三维重建的协作视图策略学习 COLMAR: Cooperative View Policy Learning for Multi-Agent Active 3D Reconstruction arXiv 2607.13524
基于竞争性多智能体强化学习的携网无人机拦截敏捷目标方法 Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning arXiv 2607.05939
平均场强化学习 Mean Field Reinforcement Learning arXiv 2607.01525
通过多模态大语言模型对策略进行视觉检查实现开放式多智能体自课程学习 Open-ended Multi-agent Autocurricula via Visual Inspection of Policies with Multi-modal LLMs arXiv 2607.08193
面向反无人机系统的基于不确定性驱动多智能体强化学习的时延感知主动三角测量 Delay-Aware Active Triangulation with Uncertainty-Driven Multi-Agent Reinforcement Learning for Counter-UAS arXiv 2607.05957
面向自主订单拣选车动态电池管理的深度强化学习方法 Deep Reinforcement Learning for Dynamic Battery Management of Autonomous Order Pickers arXiv 2607.05683
CoDiMAD：基于扩散模型的无通信多机器人协调特权蒸馏 CoDiMAD: Diffusion-Based Privileged Distillation for Communication-Free Multi-Robot Coordination arXiv 2607.09587
强化学习：从算法到基础模型 Reinforcement Learning: From Algorithms To Foundation Models arXiv 2607.17560
均衡稳定性作为Q学习者合作行为的驱动力 Equilibrium stability as a driver of cooperation among Q-learners arXiv 2607.13607
基于体制条件的大语言模型增强型合作多智能体强化学习的稳定化方法 Regime-Conditional Stabilisation of LLM-Augmented Cooperative Multi-Agent Reinforcement Learning arXiv 2607.04470
基于声学的具身人机交互：一种用于空间数据物理化的 AcoustoBots 多智能体强化学习方法 Embodied Human-Robot Interaction via Acoustics: A MARL Approach with AcoustoBots for Spatial Data Physicalization arXiv 2607.06563
星际争霸微操中基于影响力图与聚类脚本的分层强化学习 Hierarchical Reinforcement Learning in StarCraft Micromanagement with Influence Maps and Cluster-based Scripts arXiv 2606.30092

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