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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 951–960 of 1718 papers

TitleStatusHype
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization—0
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning—0
Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems—0
Quantum Multi-Agent Meta Reinforcement Learning—0
Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation—0
Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks—0
Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning—0
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games—0
Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observation—0
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning—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