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 1311–1320 of 1718 papers

TitleStatusHype
Artificial Generals Intelligence: Mastering Generals.io with Reinforcement Learning—0
Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies—0
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions—0
A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control—0
A semi-centralized multi-agent RL framework for efficient irrigation scheduling—0
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play—0
A Supervised-Learning based Hour-Ahead Demand Response of a Behavior-based HEMS approximating MILP Optimization—0
A Survey of Multi-Agent Deep Reinforcement Learning with Communication—0
Emergent Language: A Survey and Taxonomy—0
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning—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