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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 1471–1480 of 1718 papers

TitleStatusHype
PAC Reinforcement Learning Algorithm for General-Sum Markov Games—0
Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation—0
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication—0
BGC: Multi-Agent Group Belief with Graph Clustering—0
On the Convergence of Consensus Algorithms with Markovian Noise and Gradient Bias—0
Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement LearningCode0
The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line—0
REMAX: Relational Representation for Multi-Agent Exploration—0
Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells—0
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—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