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Models/Heuristic

Heuristic

Reported on 16 benchmarks across 3 tasks

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Speech6 results

  • DialogueonLinux IRC (Ch2 Kummerfeld)
    1-1
    43.4
    best: 59.7 (Linear)
  • DialogueonLinux IRC (Ch2 Kummerfeld)
    Local
    67.9
    best: 80.8 (Linear)
  • DialogueonLinux IRC (Ch2 Kummerfeld)
    Shen F-1
    50.7
    best: 63 (Linear)
  • DialogueonLinux IRC (Ch2 Elsner)
    1-1
    45.1
    best: 53.1 (Linear)
  • DialogueonLinux IRC (Ch2 Elsner)
    Local
    73.8
    best: 81.9 (Linear)
  • DialogueonLinux IRC (Ch2 Elsner)
    Shen F-1
    51.8
    best: 55.1 (Linear)

Methodology5 results

  • Multi-agent Reinforcement LearningonSMAC 3s5z_vs_3s6z
    Median Win Rate
    0
    best: 100 (ACE)
  • Multi-agent Reinforcement LearningonSMAC corridor
    Median Win Rate
    0
    best: 100 (ACE)
  • Multi-agent Reinforcement LearningonSMAC MMM2
    Median Win Rate
    0
    best: 100 (ACE)
  • Multi-agent Reinforcement LearningonSMAC 6h_vs_8z
    Median Win Rate
    0
    best: 93.75 (ACE)
  • Multi-agent Reinforcement LearningonSMAC 27m_vs_30m
    Median Win Rate
    0
    best: 91.48 (DDN)

Playing Games5 results

  • SMAConSMAC 3s5z_vs_3s6z
    Median Win Rate
    0
    best: 100 (ACE)
  • SMAConSMAC corridor
    Median Win Rate
    0
    best: 100 (ACE)
  • SMAConSMAC MMM2
    Median Win Rate
    0
    best: 100 (ACE)
  • SMAConSMAC 6h_vs_8z
    Median Win Rate
    0
    best: 93.75 (ACE)
  • SMAConSMAC 27m_vs_30m
    Median Win Rate
    0
    best: 91.48 (DDN)