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 41–50 of 1718 papers

TitleStatusHype
Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning—0
Smart Traffic Signals: Comparing MARL and Fixed-Time StrategiesCode0
Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams—0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks—0
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics—0
Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors—0
Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning—0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent 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