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ResearchFormerly WizWand

Multi-agent Reinforcement Learning on Navigation

1.38RewardVL
-0.06560.30970.6851.0603May 13, 2026
Updated 5mo ago

Evaluation results

MethodLinks
VLAttack rate=0.252026.051.38
STAttack rate=0.252026.051.36
J-MAttack rate=0.252026.051.2
J-WAttack rate=0.252026.051.16
CBTSAttack rate=0.252026.051.09
MLAttack rate=0.752026.050.97
MMRAttack rate=0.252026.050.74
NSAttack rate=0.52026.050.63
MLAttack rate=0.52026.050.61
VLAttack rate=0.52026.050.53
MMRAttack rate=0.752026.050.52
STAttack rate=0.52026.050.51
NSAttack rate=0.752026.050.5
NSAttack rate=0.252026.050.49
CBTSAttack rate=0.52026.050.46
MLAttack rate=0.252026.050.43
J-WAttack rate=0.52026.050.37
MMRAttack rate=0.52026.050.37
J-MAttack rate=0.52026.050.23
J-WAttack rate=0.752026.050.14
J-MAttack rate=0.752026.050.11
CBTSAttack rate=0.752026.050.08
VLAttack rate=0.752026.050.03
STAttack rate=0.752026.05-0.01