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 751–760 of 1718 papers

TitleStatusHype
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning—0
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
A semi-centralized multi-agent RL framework for efficient irrigation scheduling—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks—0
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios—0
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control—0
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation—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