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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 971–980 of 1718 papers

TitleStatusHype
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems—0
Regularization of the policy updates for stabilizing Mean Field Games—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center—0
Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems—0
Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay—0
Multi-agent Reinforcement Learning for Decentralized Stable Matching—0
Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms—0
Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles—0
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions—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