SOTAVerified

Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 1551–1600 of 1718 papers

TitleStatusHype
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning—0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth—0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks—0
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge—0
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning—0
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning—0
Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning—0
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games—0
Efficiently Quantifying Individual Agent Importance in Cooperative MARL—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
Efficient Replay Memory Architectures in Multi-Agent Reinforcement Learning for Traffic Congestion Control—0
Efficient Training in Multi-Agent Reinforcement Learning: A Communication-Free Framework for the Box-Pushing Problem—0
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning—0
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance—0
Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football—0
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning—0
Emergence of linguistic conventions in multi-agent reinforcement learning—0
Emergence of Roles in Robotic Teams with Model Sharing and Limited Communication—0
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning—0
Learning and communication pressures in neural networks: Lessons from emergent communication—0
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks—0
Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things—0
Emergent Communication through Negotiation—0
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning—0
Emergent Resource Exchange and Tolerated Theft Behavior using Multi-Agent Reinforcement Learning—0
Empathic Coupling of Homeostatic States for Intrinsic Prosociality—0
EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System—0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Enabling Multi-Robot Collaboration from Single-Human Guidance—0
Enabling the Wireless Metaverse via Semantic Multiverse Communication—0
EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management—0
Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks—0
Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning—0
Energy-Efficient Flying LoRa Gateways: A Multi-Agent Reinforcement Learning Approach—0
Resource Optimization for Semantic-Aware Networks with Task Offloading—0
Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control—0
Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning—0
Enhancing Multi-Agent Coordination through Common Operating Picture Integration—0
Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy—0
Enhancing the Robustness of QMIX against State-adversarial Attacks—0
Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse—0
Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified