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 81–90 of 1718 papers

TitleStatusHype
Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control—0
Attention-Augmented Inverse Reinforcement Learning with Graph Convolutions for Multi-Agent Task Allocation—0
Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement LearningCode0
HypRL: Reinforcement Learning of Control Policies for Hyperproperties—0
OrbitZoo: Multi-Agent Reinforcement Learning Environment for Orbital Dynamics—0
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning—0
An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement LearningCode0
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal ControlCode1
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning—0
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified