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 801–810 of 1718 papers

TitleStatusHype
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions—0
Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments—0
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning—0
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
Learning a Multi-Agent Controller for Shared Energy Storage System—0
Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning—0
Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning—0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization—0
Show:102550
← PrevPage 81 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