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 976–1000 of 1718 papers

TitleStatusHype
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning—0
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Uncoupled Learning of Differential Stackelberg Equilibria with CommitmentsCode0
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
Best Possible Q-Learning—0
Learning Roles with Emergent Social Value Orientations—0
Multi-Agent Congestion Cost Minimization With Linear Function ApproximationsCode0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning—0
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
Resource Optimization for Semantic-Aware Networks with Task Offloading—0
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning—0
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement LearningCode0
Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning SystemsCode0
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global StateCode0
SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads—0
Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism—0
Self-Motivated Multi-Agent ExplorationCode0
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on VideosCode0
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics—0
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning—0
Large-Scale Traffic Signal Control by a Nash Deep Q-network Approach—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