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 841–850 of 1718 papers

TitleStatusHype
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning—0
Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints—0
Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning—0
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning—0
Emergent Resource Exchange and Tolerated Theft Behavior using Multi-Agent Reinforcement Learning—0
Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning—0
Enhancing the Robustness of QMIX against State-adversarial Attacks—0
Decentralized Multi-Agent Reinforcement Learning with Global State Prediction—0
Discovering Causality for Efficient Cooperation in Multi-Agent EnvironmentsCode0
Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction—0
Show:102550
← PrevPage 85 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