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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 1561–1570 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching—0
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems—0
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized CriticsCode0
Deep Coordination GraphsCode0
Multi-Agent Actor-Critic with Hierarchical Graph Attention Network—0
Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals—0
Multi-Agent Hierarchical Reinforcement Learning for Humanoid Navigation—0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach—0
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization—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