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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 1071–1080 of 1718 papers

TitleStatusHype
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients—0
Sequential Communication in Multi-Agent Reinforcement Learning—0
Sequential Multi-objective Multi-agent Reinforcement Learning Approach for Predictive Maintenance—0
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning—0
Shapley Value Based Multi-Agent Reinforcement Learning: Theory, Method and Its Application to Energy Network—0
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles—0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning—0
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning—0
Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents—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