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 14311440 of 1718 papers

TitleStatusHype
The Emergence of Adversarial Communication in Multi-Agent Reinforcement LearningCode1
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents0
QPLEX: Duplex Dueling Multi-Agent Q-LearningCode1
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound0
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning0
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
Value-Decomposition Multi-Agent Actor-CriticsCode1
Off-Policy Multi-Agent Decomposed Policy GradientsCode1
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense0
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
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Benchmark Results

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