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 111–120 of 1718 papers

TitleStatusHype
Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control—0
Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy—0
H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic—0
Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping—0
ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement LearningCode2
Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse—0
Using a single actor to output personalized policy for different intersections—0
Q-MARL: A quantum-inspired algorithm using neural message passing for large-scale multi-agent reinforcement learning—0
Fully-Decentralized MADDPG with Networked Agents—0
Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation—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