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
DCIR: Dynamic Consistency Intrinsic Reward 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
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning—0
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios—0
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control—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