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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 211220 of 1718 papers

TitleStatusHype
POGEMA: A Benchmark Platform for Cooperative Multi-Agent PathfindingCode1
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario GenerationCode1
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy ManagementCode1
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power SystemsCode1
An Empirical Study on Google Research Football Multi-agent ScenariosCode1
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language ModelsCode1
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement LearningCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement LearningCode1
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement LearningCode1
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Benchmark Results

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