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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 961–970 of 1718 papers

TitleStatusHype
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks—0
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning—0
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances—0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
Decentralized Multi-Agent Reinforcement Learning with Global State Prediction—0
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System—0
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network—0
Decentralized Policy Optimization—0
Decentralized Q-Learning in Zero-sum Markov Games—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