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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 921–930 of 1718 papers

TitleStatusHype
Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning—0
Learning Sparse Graphon Mean Field GamesCode0
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning—0
Mean Field Games on Weighted and Directed Graphs via Colored Digraphons—0
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning—0
Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning—0
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning—0
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
A collaboration of multi-agent model using an interactive interfaceCode0
A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space—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