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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 1041–1050 of 1718 papers

TitleStatusHype
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems—0
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients—0
Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism—0
Scalable, Decentralized Multi-Agent Reinforcement Learning Methods Inspired by Stigmergy and Ant Colonies—0
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot—0
Scalable Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot Task Planning—0
Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling—0
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward—0
Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility—0
Scalable Multi-Agent Reinforcement Learning with General Utilities—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