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 971–980 of 1718 papers

TitleStatusHype
Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements—0
Decentralized scheduling through an adaptive, trading-based multi-agent system—0
Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network—0
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective—0
Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning—0
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability—0
DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies—0
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks—0
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing—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