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 801–825 of 1718 papers

TitleStatusHype
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation—0
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning—0
Offline Learning in Markov Games with General Function Approximation—0
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative Multi-Agent Reinforcement Learning—0
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are BiasedCode1
Best Possible Q-Learning—0
Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement LearningCode2
Learning Roles with Emergent Social Value Orientations—0
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Multi-Agent Congestion Cost Minimization With Linear Function ApproximationsCode0
Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning—0
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning—0
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement LearningCode0
Resource Optimization for Semantic-Aware Networks with Task Offloading—0
Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning SystemsCode0
Heterogeneous Multi-Robot Reinforcement LearningCode2
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global StateCode0
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning ProblemsCode1
Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized IntersectionsCode1
SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning—0
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads—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