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 381–390 of 1718 papers

TitleStatusHype
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising—0
A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control—0
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective—0
Best Possible Q-Learning—0
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network—0
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue—0
Decentralized scheduling through an adaptive, trading-based multi-agent system—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