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 621–630 of 1718 papers

TitleStatusHype
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles—0
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control—0
MARLIM: Multi-Agent Reinforcement Learning for Inventory Management—0
Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation—0
Robust Multi-Agent Reinforcement Learning with State UncertaintyCode1
Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach—0
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning—0
Improving International Climate Policy via Mutually Conditional Binding Commitments—0
Consensus-based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning—0
Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication—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