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

Showing 12211230 of 1718 papers

TitleStatusHype
Flip Learning: Erase to Segment0
Scalable Multi-agent Reinforcement Learning Algorithm for Wireless NetworksCode1
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement LearningCode1
Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training0
Packet Routing with Graph Attention Multi-agent Reinforcement Learning0
Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer0
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
Communicating via Markov Decision ProcessesCode0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty0
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified