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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 1611–1620 of 1718 papers

TitleStatusHype
Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximation—0
Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning—0
Experience-replay Innovative Dynamics—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning—0
Exploiting hidden structures in non-convex games for convergence to Nash equilibrium—0
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning—0
Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank—0
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain—0
Exploration with Unreliable Intrinsic Reward in 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