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 1241–1250 of 1718 papers

TitleStatusHype
Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Framework—0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
A finite time analysis of distributed Q-learning—0
A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space—0
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach—0
A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior—0
A General Framework for Learning Mean-Field Games—0
A Generative Model Enhanced Multi-Agent Reinforcement Learning Method for Electric Vehicle Charging Navigation—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