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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

Showing 1120 of 1718 papers

TitleStatusHype
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement LearningCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language ModelsCode2
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement LearningCode2
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning BenchmarksCode2
SustainDC: Benchmarking for Sustainable Data Center ControlCode2
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource AllocationCode2
Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement LearningCode2
Safe Multi-Agent Reinforcement Learning with Bilevel Optimization in Autonomous DrivingCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified