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 31–40 of 1718 papers

TitleStatusHype
RLAE: Reinforcement Learning-Assisted Ensemble for LLMs—0
Sorrel: A simple and flexible framework for multi-agent reinforcement learningCode1
Biological Pathway Guided Gene Selection Through Collaborative Reinforcement LearningCode0
Information Structure in Mappings: An Approach to Learning, Representation, and Generalisation—0
Reward-Independent Messaging for Decentralized Multi-Agent Reinforcement Learning—0
Revisiting Multi-Agent World Modeling from a Diffusion-Inspired PerspectiveCode0
The challenge of hidden gifts in multi-agent reinforcement learning—0
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications—0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks—0
Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning—0
Show:102550
← PrevPage 4 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