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 1251–1260 of 1718 papers

TitleStatusHype
Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer—0
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation—0
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms—0
Multi-agent Reinforcement Learning: A Comprehensive Survey—0
Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response—0
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges—0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition—0
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks—0
Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing—0
Multi-agent Reinforcement Learning-based Network Intrusion Detection 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