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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 661–670 of 1718 papers

TitleStatusHype
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification—0
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph—0
Multi-agent transformer-accelerated RL for satisfaction of STL specifications—0
Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update—0
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time—0
Learning and communication pressures in neural networks: Lessons from emergent communication—0
Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative DrivingCode0
Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies—0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
Strategizing against Q-learners: A Control-theoretical Approach—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