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 1401–1410 of 1718 papers

TitleStatusHype
Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning—0
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
Neural Recursive Belief States in Multi-Agent Reinforcement Learning—0
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Safe Multi-Agent Reinforcement Learning via Shielding—0
Data sharing gamesCode0
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
Show:102550
← PrevPage 141 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