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 261–270 of 1718 papers

TitleStatusHype
Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning—0
PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal ControlCode1
Evolution of Societies via Reinforcement LearningCode0
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
Scalable spectral representations for multi-agent reinforcement learning in network MDPs—0
A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language ModelsCode2
FlickerFusion: Intra-trajectory Domain Generalizing Multi-Agent RL—0
A Distributed Primal-Dual Method for Constrained Multi-agent Reinforcement Learning with General Parameterization—0
Show:102550
← PrevPage 27 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