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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 611–620 of 1718 papers

TitleStatusHype
Never Explore Repeatedly in Multi-Agent Reinforcement Learning—0
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement LearningCode0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing—0
Heterogeneous Multi-Agent Reinforcement Learning via Mirror Descent Policy OptimizationCode0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
An In-Depth Analysis of Discretization Methods for Communication Learning using Backpropagation with Multi-Agent Reinforcement Learning—0
Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning—0
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement LearningCode0
Unsynchronized Decentralized Q-Learning: Two Timescale Analysis By Persistence—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