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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 1231–1240 of 1718 papers

TitleStatusHype
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things—0
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach—0
Adaptive Learning Rates for Multi-Agent Reinforcement Learning—0
Adaptive Opponent Policy Detection in Multi-Agent MDPs: Real-Time Strategy Switch Identification Using Running Error Estimation—0
Adaptive parameter sharing for multi-agent reinforcement learning—0
AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience—0
A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning—0
A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control—0
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning—0
A Distributed Primal-Dual Method for Constrained Multi-agent Reinforcement Learning with General Parameterization—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