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 161–170 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning with Focal Diversity OptimizationCode0
Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning—0
Deep Meta Coordination Graphs for Multi-agent Reinforcement LearningCode0
Double Distillation Network for Multi-Agent Reinforcement Learning—0
Optimistic ε-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Learning Efficient Flocking Control based on Gibbs Random Fields—0
Energy-Efficient Flying LoRa Gateways: A Multi-Agent Reinforcement Learning Approach—0
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement LearningCode0
Sequential Multi-objective Multi-agent Reinforcement Learning Approach for Predictive Maintenance—0
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play—0
Show:102550
← PrevPage 17 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