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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 411–420 of 1718 papers

TitleStatusHype
PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement LearningCode0
Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control—0
Causal Mean Field Multi-Agent Reinforcement Learning—0
Enhancing Language Multi-Agent Learning with Multi-Agent Credit Re-Assignment for Interactive Environment GeneralizationCode0
Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning—0
Hypernetwork-based approach for optimal composition design in partially controlled multi-agent systems—0
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing—0
Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel DecodingCode0
Cooperative Multi-Agent Planning with Adaptive Skill Synthesis—0
Q-MARL: A GRAPH-BASED SOLUTION FOR LARGE-SCALE MULTI-AGENT REINFORCEMENT LEARNING INSPIRED BY QUANTUM CHEMISTRY—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified