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 341–350 of 1718 papers

TitleStatusHype
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