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 1271–1280 of 1718 papers

TitleStatusHype
A Multi-Agent Reinforcement Learning Framework for Evaluating the U.S. Ending the HIV Epidemic Plan—0
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning—0
A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue—0
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
An Algorithm For Adversary Aware Decentralized Networked MARL—0
Analysing Congestion Problems in Multi-agent Reinforcement Learning—0
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning—0
An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems—0
Show:102550
← PrevPage 128 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