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 1271–1280 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs—0
Multi-agent Reinforcement Learning for Dynamic Dispatching in Material Handling Systems—0
Multi-Agent Reinforcement Learning for Greenhouse Gas Offset Credit Markets—0
Multi-Agent Reinforcement Learning for Decentralized Reservoir Management via Murmuration Intelligence—0
Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study—0
Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing—0
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks—0
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment—0
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic—0
Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems—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