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SMAC

The StarCraft Multi-Agent Challenge

Introduced 2019-02-11

The StarCraft Multi-Agent Challenge (SMAC) is a benchmark that provides elements of partial observability, challenging dynamics, and high-dimensional observation spaces. SMAC is built using the StarCraft II game engine, creating a testbed for research in cooperative MARL where each game unit is an independent RL agent.

Related Benchmarks

SMAC 26m_vs_30m/Multi-agent Reinforcement Learning/Average Score SMAC 26m_vs_30m/Multi-agent Reinforcement Learning/Median Win Rate SMAC 26m_vs_30m/SMAC/Average Score SMAC 26m_vs_30m/SMAC/Median Win Rate SMAC 27m_vs_30m/Multi-agent Reinforcement Learning/Average Score SMAC 27m_vs_30m/Multi-agent Reinforcement Learning/Median Win Rate SMAC 27m_vs_30m/SMAC/Average Score SMAC 27m_vs_30m/SMAC/Median Win Rate SMAC 3s5z_vs_3s6z/Multi-agent Reinforcement Learning/Average Score SMAC 3s5z_vs_3s6z/Multi-agent Reinforcement Learning/Median Win Rate SMAC 3s5z_vs_3s6z/SMAC/Average Score SMAC 3s5z_vs_3s6z/SMAC/Median Win Rate SMAC 3s5z_vs_4s6z/Multi-agent Reinforcement Learning/Average Score SMAC 3s5z_vs_4s6z/Multi-agent Reinforcement Learning/Median Win Rate SMAC 3s5z_vs_4s6z/SMAC/Average Score SMAC 3s5z_vs_4s6z/SMAC/Median Win Rate SMAC 6h_vs_8z/Multi-agent Reinforcement Learning/Average Score SMAC 6h_vs_8z/Multi-agent Reinforcement Learning/Median Win Rate SMAC 6h_vs_8z/SMAC/Average Score SMAC 6h_vs_8z/SMAC/Median Win Rate SMAC 6h_vs_9z/Multi-agent Reinforcement Learning/Average Score SMAC 6h_vs_9z/Multi-agent Reinforcement Learning/Median Win Rate SMAC 6h_vs_9z/SMAC/Average Score SMAC 6h_vs_9z/SMAC/Median Win Rate SMAC MMM2/Multi-agent Reinforcement Learning/Average Score SMAC MMM2/Multi-agent Reinforcement Learning/Median Win Rate SMAC MMM2/SMAC/Average Score SMAC MMM2/SMAC/Median Win Rate SMAC MMM2_7m2M1M_vs_8m4M1M/Multi-agent Reinforcement Learning/Average Score SMAC MMM2_7m2M1M_vs_8m4M1M/Multi-agent Reinforcement Learning/Median Win Rate SMAC MMM2_7m2M1M_vs_8m4M1M/SMAC/Average Score SMAC MMM2_7m2M1M_vs_8m4M1M/SMAC/Median Win Rate SMAC MMM2_7m2M1M_vs_9m3M1M/Multi-agent Reinforcement Learning/Average Score SMAC MMM2_7m2M1M_vs_9m3M1M/Multi-agent Reinforcement Learning/Median Win Rate SMAC MMM2_7m2M1M_vs_9m3M1M/SMAC/Average Score SMAC MMM2_7m2M1M_vs_9m3M1M/SMAC/Median Win Rate SMAC corridor/Multi-agent Reinforcement Learning/Average Score SMAC corridor/Multi-agent Reinforcement Learning/Median Win Rate SMAC corridor/SMAC/Average Score SMAC corridor/SMAC/Median Win Rate SMAC corridor_2z_vs_24zg/Multi-agent Reinforcement Learning/Average Score SMAC corridor_2z_vs_24zg/Multi-agent Reinforcement Learning/Median Win Rate SMAC corridor_2z_vs_24zg/SMAC/Average Score SMAC corridor_2z_vs_24zg/SMAC/Median Win Rate SMAC-Exp/Multi-agent Reinforcement Learning/Median Win Rate SMAC-Exp/Real-Time Strategy Games/Median Win Rate SMAC-Exp/Video Games/Median Win Rate

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Papers: 324
Benchmarks: 0

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