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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 311–320 of 1718 papers

TitleStatusHype
An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management—0
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems—0
A finite time analysis of distributed Q-learning—0
Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control—0
Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks—0
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning—0
A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives—0
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility—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