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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 791–800 of 1718 papers

TitleStatusHype
Learning Homophilic Incentives in Sequential Social Dilemmas—0
LQR with Tracking: A Zeroth-order Approach and Its Global Convergence—0
Federated Learning for Distributed Energy-Efficient Resource Allocation—0
Large-Scale Traffic Signal Control by a Nash Deep Q-network Approach—0
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming—0
Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control—0
Last Iterate Convergence in Monotone Mean Field Games—0
Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning—0
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning—0
Federated Dynamic Spectrum Access—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