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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
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience—0
Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication—0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition—0
Dealing with Non-Stationarity in Multi-Agent Deep 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