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 61–70 of 1718 papers

TitleStatusHype
Adaptive and Robust DBSCAN with Multi-agent Reinforcement LearningCode0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning—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
Safe Bottom-Up Flexibility Provision from Distributed Energy Resources—0
Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A SurveyCode2
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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