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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 491–500 of 1718 papers

TitleStatusHype
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—0
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning—0
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances—0
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning—0
AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience—0
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks—0
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning—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