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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 571–580 of 1718 papers

TitleStatusHype
CORD: Generalizable Cooperation via Role Diversity—0
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems—0
CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients—0
Coordination Failure in Cooperative Offline MARL—0
Attention-Augmented Inverse Reinforcement Learning with Graph Convolutions for Multi-Agent Task Allocation—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation—0
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games—0
Coordination-driven learning in multi-agent problem spaces—0
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—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