SOTAVerified

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 1131–1140 of 1718 papers

TitleStatusHype
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent ProblemsCode1
Multi Agent Reinforcement Learning Trajectory Design and Two-Stage Resource Management in CoMP UAV VLC Networks—0
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel—0
A Supervised-Learning based Hour-Ahead Demand Response of a Behavior-based HEMS approximating MILP Optimization—0
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach—0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—0
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—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