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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

Showing 171180 of 1718 papers

TitleStatusHype
Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement LearningCode1
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement LearningCode1
ALMA: Hierarchical Learning for Composite Multi-Agent TasksCode1
Information Design in Multi-Agent Reinforcement LearningCode1
CoLight: Learning Network-level Cooperation for Traffic Signal ControlCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?Code1
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?Code1
A MARL Based Multi-Target Tracking Algorithm Under Jamming Against RadarCode1
Collaborating with Humans without Human DataCode1
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Benchmark Results

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