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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 711–720 of 1718 papers

TitleStatusHype
SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning—0
Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-AttentionCode0
Stubborn: An Environment for Evaluating Stubbornness between Agents with Aligned IncentivesCode0
SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control—0
Mastering Asymmetrical Multiplayer Game with Multi-Agent Asymmetric-Evolution Reinforcement Learning—0
Interpretability for Conditional Coordinated Behavior in Multi-Agent Reinforcement Learning—0
Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning—0
Heterogeneous-Agent Reinforcement LearningCode2
Graph Exploration for Effective Multi-agent Q-Learning—0
Cooperative Multi-Agent Reinforcement Learning for Inventory Management—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