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

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Showing 241–250 of 1718 papers

TitleStatusHype
Empathic Coupling of Homeostatic States for Intrinsic Prosociality—0
Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control—0
InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemmaCode1
DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach—0
Exploring Multi-Agent Reinforcement Learning for Unrelated Parallel Machine Scheduling—0
Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing—0
A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs—0
OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control—0
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement LearningCode1
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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