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 1571–1580 of 1718 papers

TitleStatusHype
Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net—0
Stock market microstructure inference via multi-agent reinforcement learning—0
Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication—0
On Memory Mechanism in Multi-Agent Reinforcement Learning—0
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning—0
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based ControlCode0
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control—0
Universal Policies to Learn Them AllCode0
Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning—0
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking—0
Show:102550
← PrevPage 158 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