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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 1121–1130 of 1718 papers

TitleStatusHype
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration—0
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning—0
Causal Multi-Agent Reinforcement Learning: Review and Open Problems—0
Resilient Consensus-based Multi-agent Reinforcement Learning with Function ApproximationCode1
Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium controlCode0
Collaboration Promotes Group Resilience in Multi-Agent AI—0
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic—0
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement LearningCode3
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement LearningCode0
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power SystemsCode1
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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