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 421–430 of 1718 papers

TitleStatusHype
N-Agent Ad Hoc TeamworkCode1
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
Differentially Private Reinforcement Learning with Self-Play—0
Attention-Driven Multi-Agent Reinforcement Learning: Enhancing Decisions with Expertise-Informed Tasks—0
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration—0
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks—0
Laser Learning Environment: A new environment for coordination-critical multi-agent tasksCode1
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search—0
EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management—0
Distributed Autonomous Swarm Formation for Dynamic Network Bridging—0
Show:102550
← PrevPage 43 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