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 751–800 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
MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits—0
Fact-based Agent modeling for Multi-Agent Reinforcement Learning—0
Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control—0
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization—0
Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning—0
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization—0
Sample-Efficient Multi-Agent RL: An Optimization Perspective—0
Replication of Multi-agent Reinforcement Learning for the "Hide and Seek" Problem—0
FP3O: Enabling Proximal Policy Optimization in Multi-Agent Cooperation with Parameter-Sharing Versatility—0
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning—0
Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning—0
Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning—0
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games—0
A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling—0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control—0
COMPOSER: Scalable and Robust Modular Policies for Snake Robots—0
Cooperation Dynamics in Multi-Agent Systems: Exploring Game-Theoretic Scenarios with Mean-Field EquilibriaCode0
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach—0
Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem—0
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing—0
Characterizing Speed Performance of Multi-Agent Reinforcement Learning—0
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning—0
Attention Loss Adjusted Prioritized Experience Replay—0
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks—0
Dynamic Handover: Throw and Catch with Bimanual Hands—0
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate ConvergenceCode0
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning—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