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

Showing 125 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning for Autonomous Driving: A SurveyCode5
SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion PlanningCode4
Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and EvaluationCode3
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning LibraryCode3
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement LearningCode3
Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement LearningCode3
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement LearningCode2
MAexp: A Generic Platform for RL-based Multi-Agent ExplorationCode2
Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement LearningCode2
Heterogeneous-Agent Reinforcement LearningCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter ControlCode2
Ensembling Prioritized Hybrid Policies for Multi-agent PathfindingCode2
Maximum Entropy Heterogeneous-Agent Reinforcement LearningCode2
Heterogeneous Multi-Robot Reinforcement LearningCode2
Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource AllocationCode2
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement LearningCode2
Deep Reinforcement Learning for Multi-Agent InteractionCode2
A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language ModelsCode2
Emergent Reciprocity and Team Formation from Randomized Uncertain Social PreferencesCode2
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-DependencyCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement LearningCode2
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement LearningCode2
Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object TrackingCode2
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Benchmark Results

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