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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
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning—0
Selective Reincarnation: Offline-to-Online Multi-Agent Reinforcement Learning—0
Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning—0
Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution—0
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation—0
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
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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