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Reinforcement learning is a machine learning paradigm which has a number of applications in gaming, stock prediction, robot navigation etc. The reinforcement learning can be applied to complex real-world tasks which have adjustable problem spaces. In this paper a novel approach called Q-HeteLearn a Progressive Learning method is introduced to classify the objects by traversing the meta-paths in the Heterogeneous Information Networks. The proposed approach showed best results when compared to adoi:10.30534/ijeter/2020/35832020 fatcat:m6xvtws7mzhyzb526w4eme57da