  The deep reinforcement learning method usually requires a large number of
training images and executing actions to obtain sufficient results. When it is
extended a real-task in the real environment with an actual robot, the method
will be required more training images due to complexities or noises of the
input images, and executing a lot of actions on the real robot also becomes a
serious problem. Therefore, we propose an extended deep reinforcement learning
method that is applied a generative model to initialize the network for
reducing the number of training trials. In this paper, we used a deep q-network
method as the deep reinforcement learning method and a deep auto-encoder as the
generative model. We conducted experiments on three different tasks: a
cart-pole game, an atari game, and a real-game with an actual robot. The
proposed method trained efficiently on all tasks than the previous method,
especially 2.5 times faster on a task with real environment images.
