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Papers with Code · CC BY-SA 4.0

Multi-agent Reinforcement Learning

237 평가 기록 30 datasets

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.

<span class="description-source">Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports </span>

개요전체 데이터셋논문·평가 기록하위 태스크

데이터셋

30개
SMAC 6h_vs_8zSMAC MMM2SMAC 3s5z_vs_3s6zSMAC corridorDef_Armored_sequentialDef_Infantry_sequentialDef_Outnumbered_sequentialSMAC 27m_vs_30mDef_Armored_parallelDef_Infantry_parallelDef_Outnumbered_parallelOff_Complicated_parallelOff_Distant_parallelOff_Hard_parallelOff_Near_parallelOff_Superhard_parallelSMAC 26m_vs_30mSMAC 3s5z_vs_4s6zSMAC 6h_vs_9zSMAC MMM2_7m2M1M_vs_8m4M1MSMAC MMM2_7m2M1M_vs_9m3M1MSMAC corridor_2z_vs_24zgOff_Complicated_sequentialOff_Distant_sequentialOff_Hard_sequentialOff_Near_sequentialOff_Superhard_sequentialParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics
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