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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 1231–1240 of 1718 papers

TitleStatusHype
Multi-agent Databases via Independent Learning—0
Multi-agent Deep Covering Skill Discovery—0
Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning—0
Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem—0
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations—0
Multi-Agent Deep Reinforcement Learning with Adaptive Policies—0
Multi-Agent Diagnostics for Robustness via Illuminated Diversity—0
Multi-Agent Game Abstraction via Graph Attention Neural Network—0
Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance—0
Multi-Agent Hierarchical Reinforcement Learning for Humanoid Navigation—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