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 551–575 of 1718 papers

TitleStatusHype
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space—0
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems—0
Dynamic Pricing in High-Speed Railways Using Multi-Agent Reinforcement Learning—0
Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees—0
Dynamic Handover: Throw and Catch with Bimanual Hands—0
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation—0
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems—0
An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management—0
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative Multi-Agent Reinforcement Learning—0
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control—0
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning—0
Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks—0
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility—0
A finite time analysis of distributed Q-learning—0
Double Distillation Network for Multi-Agent Reinforcement Learning—0
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients—0
An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning—0
DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach—0
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility—0
Diverse Conventions for Human-AI Collaboration—0
Divergence-Regularized Multi-Agent Actor-Critic—0
Center of Gravity-Guided Focusing Influence Mechanism for Multi-Agent Reinforcement Learning—0
An In-Depth Analysis of Discretization Methods for Communication Learning using Backpropagation with Multi-Agent Reinforcement Learning—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