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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 291–300 of 1718 papers

TitleStatusHype
Multi-agent Reinforcement Learning for Dynamic Dispatching in Material Handling Systems—0
Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement LearningCode1
Online Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning—0
PathSeeker: Exploring LLM Security Vulnerabilities with a Reinforcement Learning-Based Jailbreak Approach—0
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling—0
On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks—0
HARP: Human-Assisted Regrouping with Permutation Invariant Critic for Multi-Agent Reinforcement LearningCode0
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning—0
DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training—0
Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Framework—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