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 1341–1350 of 1718 papers

TitleStatusHype
Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator—0
Navigating the Smog: A Cooperative Multi-Agent RL for Accurate Air Pollution Mapping through Data Assimilation—0
Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints—0
Negotiated Reasoning: On Provably Addressing Relative Over-Generalization—0
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning—0
Networked Agents in the Dark: Team Value Learning under Partial Observability—0
Networked Multi-Agent Reinforcement Learning with Emergent Communication—0
Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approach—0
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning—0
The impact of behavioral diversity in multi-agent reinforcement learning—0
Show:102550
← PrevPage 135 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