QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

Benchmark Model Rank Results
smac-on-smac-def-armored-parallelQTRAN#4Median Win Rate: 5.0
smac-on-smac-def-armored-sequentialQTRAN#2Median Win Rate: 93.8
smac-on-smac-def-infantry-parallelQTRAN#1Median Win Rate: 100.0
smac-on-smac-def-infantry-sequentialQTRAN#2Median Win Rate: 100
smac-on-smac-def-outnumbered-parallelQTRAN#5Median Win Rate: 0.0
smac-on-smac-def-outnumbered-sequentialQTRAN#2Median Win Rate: 81.3
smac-on-smac-off-complicated-parallelQTRAN#4Median Win Rate: 0.0
smac-on-smac-off-distant-parallelQTRAN#6Median Win Rate: 0.0
smac-on-smac-off-hard-parallelQTRAN#4Median Win Rate: 0.0
smac-on-smac-off-near-parallelQTRAN#4Median Win Rate: 0.0
smac-on-smac-off-superhard-parallelQTRAN#4Median Win Rate: 0.0