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 326–350 of 1718 papers

TitleStatusHype
Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams—0
Smart Traffic Signals: Comparing MARL and Fixed-Time StrategiesCode0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks—0
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics—0
Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors—0
Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning—0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles—0
Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning—0
Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning—0
Multi-source Plume Tracing via Multi-Agent Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue—0
Bi-level Mean Field: Dynamic Grouping for Large-Scale MARL—0
Offline Multi-agent Reinforcement Learning via Score Decomposition—0
Learning Power Control Protocol for In-Factory 6G Subnetworks—0
CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution—0
Adaptive and Robust DBSCAN with Multi-agent Reinforcement LearningCode0
Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning—0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observation—0
Interpretable Emergent Language Using Inter-Agent TransformersCode0
Securing 5G and Beyond-Enabled UAV Networks: Resilience Through Multiagent Learning and Transformers Detection—0
Emergence of Roles in Robotic Teams with Model Sharing and Limited Communication—0
Safe and Efficient CAV Lane Changing using Decentralised Safety Shields—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

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