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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 861–870 of 1718 papers

TitleStatusHype
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning—0
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning—0
TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement LearningCode0
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning—0
Revealing Robust Oil and Gas Company Macro-Strategies using Deep Multi-Agent Reinforcement Learning—0
Credit-cognisant reinforcement learning for multi-agent cooperation—0
Explainable Action Advising for Multi-Agent Reinforcement LearningCode0
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified