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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 1481–1490 of 1718 papers

TitleStatusHype
Gifting in multi-agent reinforcement learningCode0
Multi-agent Reinforcement Learning for Decentralized Stable Matching—0
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information—0
Learning Expensive Coordination: An Event-Based Deep RL Approach—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
Macro-Action-Based Deep Multi-Agent Reinforcement Learning—0
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction LibraryCode1
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning—0
Networked Multi-Agent Reinforcement Learning with Emergent Communication—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