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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
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games—0
Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning—0
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning—0
Few-Shot Teamwork—0
Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks—0
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios—0
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration—0
Interpretability for Conditional Coordinated Behavior in Multi-Agent Reinforcement Learning—0
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network—0
Interpreting Graph Drawing with Multi-Agent Reinforcement Learning—0
Intersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
A semi-centralized multi-agent RL framework for efficient irrigation scheduling—0
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control—0
Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements—0
Decentralized Q-Learning in Zero-sum Markov Games—0
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation—0
Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum—0
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding—0
A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control—0
Abstracting Geo-specific Terrains to Scale Up 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