  The research on deep reinforcement learning which estimates Q-value by deep
learning has been attracted the interest of researchers recently. In deep
reinforcement learning, it is important to efficiently learn the experiences
that an agent has collected by exploring environment. We propose NEC2DQN that
improves learning speed of a poor sample efficiency algorithm such as DQN by
using good one such as NEC at the beginning of learning. We show it is able to
learn faster than Double DQN or N-step DQN in the experiments of Pong.
