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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 611–620 of 1718 papers

TitleStatusHype
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
Discrete-Time Mean Field Control with Environment States—0
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning—0
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective—0
Directly Attention Loss Adjusted Prioritized Experience Replay—0
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning—0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods—0
A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making—0
Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Framework—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