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 1011–1020 of 1718 papers

TitleStatusHype
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense—0
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach—0
Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach—0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents—0
Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning—0
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning—0
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL—0
Show:102550
← PrevPage 102 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