This paper presents a method for searching for the optimal paths for autonomously moving agents in mazes by modified Learning Vector Quantization (LVQ) in a reinforcement learning framework. LVQ algorithm is faster than Q-learning algorithms because LVQ concentrates on the best behavior in available behaviors while Q-learning algorithms calculate values of all available behaviors and choose the best behavior among them. However, ordinary LVQ sometimes mis-learns in the reinforcement learning environment due to erroneous teacher signals. Here a new LVQ algorithm is proposed to overcome this problem, which finds the optimal path more efficiently.