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 176200 of 1718 papers

TitleStatusHype
JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 MinutesCode1
Language Instructed Reinforcement Learning for Human-AI CoordinationCode1
Laser Learning Environment: A new environment for coordination-critical multi-agent tasksCode1
A MARL Based Multi-Target Tracking Algorithm Under Jamming Against RadarCode1
A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory ManagementCode1
Learning Individually Inferred Communication for Multi-Agent CooperationCode1
Learning Multi-Agent Communication through Structured Attentive ReasoningCode1
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural NetworkCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Learning to Share in Multi-Agent Reinforcement LearningCode1
Learning to Simulate Self-Driven Particles System with Coordinated Policy OptimizationCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement LearningCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Mava: a research library for distributed multi-agent reinforcement learning in JAXCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Collaborating with Humans without Human DataCode1
CoLight: Learning Network-level Cooperation for Traffic Signal ControlCode1
A multi-agent reinforcement learning model of common-pool resource appropriationCode1
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT NetworksCode1
More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy FactorizationCode1
Collaborative Visual NavigationCode1
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Benchmark Results

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