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 301–310 of 1718 papers

TitleStatusHype
Homeostatic Coupling for Prosocial Behavior—0
Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning—0
Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow—0
Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation—0
When Is Diversity Rewarded in Cooperative Multi-Agent Learning?—0
Ego-centric Learning of Communicative World Models for Autonomous Driving—0
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments—0
Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games—0
A MARL-based Approach for Easing MAS Organization Engineering—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