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

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Showing 751–775 of 1718 papers

TitleStatusHype
DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training—0
Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning—0
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning—0
Impression Allocation and Policy Search in Display Advertising—0
Implementations that Matter in Cooperative Multi-Agent Reinforcement Learning—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning—0
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem—0
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration—0
Interpretability for Conditional Coordinated Behavior in Multi-Agent Reinforcement Learning—0
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning—0
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
Intersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach—0
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control—0
AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL—0
IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data—0
HypRL: Reinforcement Learning of Control Policies for Hyperproperties—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing—0
Hypernetwork-based approach for optimal composition design in partially controlled multi-agent systems—0
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
CURO: Curriculum Learning for Relative Overgeneralization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified