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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 1031–1040 of 1718 papers

TitleStatusHype
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning—0
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning—0
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit DesignCode1
Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects—0
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty—0
A Survey of Multi-Agent Deep Reinforcement Learning with Communication—0
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information CollaborationCode1
Backpropagation through Time and Space: Learning Numerical Methods with 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