In reinforcement learning, environments with a sparse reward signal are significantly difficult to model. Especially, learning actions in 3D environment from the first person view is regarded as POMDP which potentially extends state space. Large environments with a sparse reward need efficient learning process in large state space. In this paper, we propose a deep reinforcement learning method with the memory module proposed in Neural Episodic Control, adding cognitive information to the memory module to improve performance.