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 691–700 of 1718 papers

TitleStatusHype
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control—0
Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems—0
Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular NetworksCode0
Multi-Agent Reinforcement Learning for Network Routing in Integrated Access Backhaul Networks—0
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges—0
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation—0
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackersCode1
Mixture of personality improved Spiking actor network for efficient multi-agent cooperation—0
An Algorithm For Adversary Aware Decentralized Networked MARL—0
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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