Papers Multi-agent Reinforcement Learning
“Multi-agent Reinforcement Learning” 태그가 달린 논문 2,244편 · 필터 해제
DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat
Multi-Agent Reinforcement Learning (MARL) has emerged as a pivotal paradigm for complex decision-making in autonomous systems and air combat. While MARL has demonstrated significant potential in air combat, achieving sop…
Multi-agent Reinforcement LearningMulti-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response
This study develops a deep reinforcement learning framework for training Unmanned Aerial Vehicle (UAV) agents to navigate and monitor simulated wildfire environments. Results show that agents learn increasingly stable an…
Multi-agent Reinforcement LearningOnline Change-point Detection for Cooperative Multi-Agent Reinforcement Learning
Cooperative multi-agent reinforcement learning (MARL) systems rely on past experience for learning coordinated behaviour, but this experience may become unreliable if the environment or task objective changes during trai…
Multi-agent Reinforcement LearningLow-Altitude Fluid Antenna Network with Multi-Agent Reinforcement Learning
Low-altitude wireless networks (LAWNs) integrate terrestrial and aerial platforms to provide ubiquitous communication, sensing, and localization services for unmanned aerial vehicles (UAVs) and electric vertical takeoff …
Multi-agent Reinforcement LearningTransfer LearningAI agents in Algorithmic Electricity Markets: On the Emergence of Tacit Collusion
As electricity market participants increasingly adopt learning-based agents for their bidding strategies, electricity markets are becoming algorithmic. Evidence from algorithmic markets in other domains shows that tacit …
Multi-agent Reinforcement LearningSIGMA: Structured Noise-Effect-Aware Grouped Multi-Agent Aggregation
Cooperative multi-agent reinforcement learning (MARL) faces significant challenges in maintaining robust coordination under noisy observations. Although observation disturbances are often introduced independently across …
Multi-agent Reinforcement LearningStarcraft IICooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data
Federated Learning (FL) enables distributed training of machine learning models while preserving data privacy. However, FL struggles with heterogeneous, non-IID client data distributions, resulting in sub-optimal and bia…
Multi-agent Reinforcement LearningFederated LearningRoboStriker: Latent-Space Strategic Games for Autonomous Humanoid Boxing
Achieving human-level competitive intelligence and physical agility in humanoid robots remains a profound challenge, particularly in contact-rich and highly dynamic tasks such as boxing. While Multi-Agent Reinforcement L…
Multi-agent Reinforcement LearningOGR-MARL: Option-Guided Residual Multi-Agent Reinforcement Learning for Heterogeneous USV Cooperative Pursuit in Constrained Port Waterways
Heterogeneous USV cooperative pursuit in constrained port waterways requires evader interception under navigation, traffic, and role constraints. This paper proposes OGR-MARL, an option-guided residual multi-agent reinfo…
Multi-agent Reinforcement LearningOne Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL
Multi-agent reinforcement learning for human-AI interaction typically relies on a single large language model to simulate user behavior. We show that this approach systematically fails to generalize, and trace the failur…
Multi-agent Reinforcement LearningIs Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details
AI agents deployed in real-world settings must be capable of coordinating with humans and other AI agents they have not encountered before. Zero-shot coordination (ZSC) algorithms aim to achieve this by specifying high-l…
Multi-agent Reinforcement LearningHetGPS: Scalable Graph Multi-Agent Reinforcement Learning with Physics-Anchored Adaptive Safety for EV Charging
Safety interventions for large populations of network-coupled agents must protect shared constraints without unnecessarily overriding task-oriented policy decisions. We present HetGPS, a hybrid graph-control framework sy…
Multi-agent Reinforcement LearningMARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation
Credit assignment is a fundamental challenge in cooperative multi-agent reinforcement learning, particularly in embodied AI settings characterized by limited and delayed feedback as well as dynamically changing numbers o…
Multi-agent Reinforcement LearningPLATO: Pointer Learner for Agent and Task Openness
Open agent systems (OASYS) are increasingly prevalent in real-world domains where the sets of agents and tasks change unpredictably over time. Such openness, including agent openness (AO) and task openness (TO), poses a …
Multi-agent Reinforcement LearningZero-shot GeneralizationGraph Neural NetworkTRUAV: Distributed Multi-Agent Reinforcement Learning for Trajectory Planning and Routing Enhancement in UAV-Aided IoT-Enabled VANETs
Unmanned aerial vehicles (UAVs) have emerged as a key enabler of next-generation Internet of Things (IoT) ecosystems, offering flexible aerial relaying to extend connectivity across dynamic vehicular ad hoc networks (VAN…
Multi-agent Reinforcement LearningTrajectory PlanningCompact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
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 pr…
Multi-agent Reinforcement LearningAutonomous VehiclesCoordinating from Memory: Graph-Structured Experience Reuse for Multi-Agent Adaptation in Dynamic Manufacturing
Dynamic manufacturing environments require multi-agent systems to coordinate effectively under frequent operational disturbances such as machine failures, urgent job arrivals, and processing time variations. Existing mul…
Multi-agent Reinforcement LearningGraph Neural NetworkDreamer-CPC: Message Learning with World Models for Decentralized Multi-agent Reinforcement Learning
In multi-agent reinforcement learning (MARL), inter-agent communication is effective for improving performance under partial observability. Representation learning-based approaches enable decentralized agents to learn me…
Multi-agent Reinforcement LearningRepresentation LearningA Self-Evolving Default Action for Cooperative Tasks with Continuous Action Space
Counterfactual credit assignment has proven effective in multi-agent reinforcement learning (MARL) for discrete action spaces, yet its extension to continuous-action cooperative tasks remains challenging. Existing method…
Multi-agent Reinforcement LearningMIND-CAVs: Multi-Intelligence Negotiation and Decision System for CAVs based on Intent-Driven Autonomy
Modern autonomous vehicles largely operate as isolated agents: they rely on on-board perception and decision modules and broadcast Basic Safety Messages (BSMs) that expose only low-level kinematic state. While existing c…
Multi-agent Reinforcement LearningAutonomous Vehicles