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 1681–1690 of 1718 papers

TitleStatusHype
Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs—0
Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication—0
Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents—0
Graph Exploration for Effective Multi-agent Q-Learning—0
Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning—0
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management—0
Show:102550
← PrevPage 169 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