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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1431–1440 of 1718 papers

TitleStatusHype
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information—0
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning—0
Flatland-RL : Multi-Agent Reinforcement Learning on Trains—0
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems—0
Minimax Sample Complexity for Turn-based Stochastic Game—0
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control—0
Show:102550
← PrevPage 144 of 172Next →
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