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By simulating the psychological and neurological system, deep reinforcement learning method has been playing an important role in the development and application of artificial intelligence with the help of the powerful feature representation capability of deep neural networks. The deep Q network which improves traditional RL methods by breaking out the learning mechanism of value function approximation and policy search based on shallow structure, has the capabilities of hierarchical feature extraction and accurate Q value approximation in various high-dimensional sensing environments.

Transfer Learning Approach for Fast Convergence of Deep Q Networks in Game Pong