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SotA/Methodology/Multi-agent Reinforcement Learning

Multi-agent Reinforcement Learning

41 benchmarks1718 papers

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>

Benchmarks

Multi-agent Reinforcement Learning on SMAC MMM2

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Multi-agent Reinforcement Learning on SMAC 3s5z_vs_3s6z

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Multi-agent Reinforcement Learning on SMAC 6h_vs_8z

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Multi-agent Reinforcement Learning on SMAC corridor

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Multi-agent Reinforcement Learning on Def_Armored_sequential

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Multi-agent Reinforcement Learning on Def_Infantry_sequential

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Multi-agent Reinforcement Learning on Def_Outnumbered_sequential

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Multi-agent Reinforcement Learning on SMAC 27m_vs_30m

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Multi-agent Reinforcement Learning on Def_Armored_parallel

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Multi-agent Reinforcement Learning on Def_Infantry_parallel

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Multi-agent Reinforcement Learning on Def_Outnumbered_parallel

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Multi-agent Reinforcement Learning on Off_Complicated_parallel

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Multi-agent Reinforcement Learning on Off_Distant_parallel

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Multi-agent Reinforcement Learning on Off_Hard_parallel

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Multi-agent Reinforcement Learning on Off_Near_parallel

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Multi-agent Reinforcement Learning on Off_Superhard_parallel

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Multi-agent Reinforcement Learning on SMAC 26m_vs_30m

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Multi-agent Reinforcement Learning on SMAC 3s5z_vs_4s6z

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Multi-agent Reinforcement Learning on SMAC 6h_vs_9z

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Multi-agent Reinforcement Learning on SMAC MMM2_7m2M1M_vs_8m4M1M

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Multi-agent Reinforcement Learning on SMAC MMM2_7m2M1M_vs_9m3M1M

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Multi-agent Reinforcement Learning on SMAC corridor_2z_vs_24zg

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Multi-agent Reinforcement Learning on Off_Complicated_sequential

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Multi-agent Reinforcement Learning on Off_Distant_sequential

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Multi-agent Reinforcement Learning on Off_Hard_sequential

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Multi-agent Reinforcement Learning on Off_Near_sequential

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Multi-agent Reinforcement Learning on Off_Superhard_sequential

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Multi-agent Reinforcement Learning on ParticleEnvs Cooperative Communication

final agent reward

Multi-agent Reinforcement Learning on SMAC-Exp

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Multi-agent Reinforcement Learning on UAV Logistics

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