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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 1301–1310 of 1718 papers

TitleStatusHype
Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable—0
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation—0
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization—0
Major-Minor Mean Field Multi-Agent Reinforcement Learning—0
Multi-agent Reinforcement Learning with Sparse Interactions by Negotiation and Knowledge Transfer—0
Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management—0
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management—0
Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization—0
Safety-Aware Multi-Agent Learning for Dynamic Network Bridging—0
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing—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