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 331–340 of 1718 papers

TitleStatusHype
Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing—0
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
Efficient Replay Memory Architectures in Multi-Agent Reinforcement Learning for Traffic Congestion Control—0
POGEMA: A Benchmark Platform for Cooperative Multi-Agent PathfindingCode1
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models—0
Navigating the Smog: A Cooperative Multi-Agent RL for Accurate Air Pollution Mapping through Data Assimilation—0
Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource AllocationCode2
Cooperative Reward Shaping for Multi-Agent Pathfinding—0
Ontology-driven Reinforcement Learning for Personalized Student Support—0
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network—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