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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 1281–1290 of 1718 papers

TitleStatusHype
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games—0
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?—0
Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance—0
Divergence-Regularized Multi-Agent Actor-Critic—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance—0
Evaluating Robustness of Cooperative MARL—0
Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning—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