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
Directly Attention Loss Adjusted Prioritized Experience Replay—0
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach—0
Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning—0
Multi-agent Attacks for Black-box Social Recommendations—0
Multi-Agent Quantum Reinforcement Learning using Evolutionary OptimizationCode0
Social Motion Prediction with Cognitive Hierarchies—0
Enhancing Multi-Agent Coordination through Common Operating Picture Integration—0
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning—0
Kindness in Multi-Agent Reinforcement Learning—0
AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent Surfaces: Deployment, Prototyping, Experiments, and Opportunities—0
Environmental-Impact Based Multi-Agent Reinforcement Learning—0
Learning Independently from Causality in Multi-Agent Environments—0
A Multi-Agent Reinforcement Learning Framework for Evaluating the U.S. Ending the HIV Epidemic Plan—0
QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning—0
Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs—0
Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach—0
MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning—0
AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning—0
Diverse Conventions for Human-AI Collaboration—0
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization—0
DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average ConstraintsCode0
Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning—0
Fact-based Agent modeling for Multi-Agent Reinforcement Learning—0
MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits—0
Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

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