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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 21–30 of 1718 papers

TitleStatusHype
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
Ego-centric Learning of Communicative World Models for Autonomous Driving—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
Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods—0
CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients—0
LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation—0
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation—0
Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning—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