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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 681–690 of 1718 papers

TitleStatusHype
Attention Schema in Neural Agents—0
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?Code1
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem—0
Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning—0
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning—0
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning—0
Pragmatic Reasoning in Structured Signaling Games—0
Explainable Multi-Agent Reinforcement Learning for Temporal QueriesCode0
Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges—0
An Empirical Study on Google Research Football Multi-agent ScenariosCode1
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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