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 76100 of 1718 papers

TitleStatusHype
Reinforced Prompt Personalization for Recommendation with Large Language ModelsCode1
POGEMA: A Benchmark Platform for Cooperative Multi-Agent PathfindingCode1
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language ModelsCode1
Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution NetworksCode1
Soft-QMIX: Integrating Maximum Entropy For Monotonic Value Function FactorizationCode1
Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement LearningCode1
PyTAG: Tabletop Games for Multi-Agent Reinforcement LearningCode1
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement LearningCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Efficient Multi-agent Reinforcement Learning by PlanningCode1
Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement LearningCode1
Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based ModellingCode1
Group-Aware Coordination Graph for Multi-Agent Reinforcement LearningCode1
N-Agent Ad Hoc TeamworkCode1
Laser Learning Environment: A new environment for coordination-critical multi-agent tasksCode1
Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message PassingCode1
Settling Decentralized Multi-Agent Coordinated Exploration by Novelty SharingCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
Multi-Agent Reinforcement Learning via Distributed MPC as a Function ApproximatorCode1
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value FactorizationCode1
Selectively Sharing Experiences Improves Multi-Agent Reinforcement LearningCode1
Multi-Agent Reinforcement Learning-Based UAV Pathfinding for Obstacle Avoidance in Stochastic EnvironmentCode1
Theory of Mind for Multi-Agent Collaboration via Large Language ModelsCode1
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement LearningCode1
Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy RegularizationCode1
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Benchmark Results

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