标题： DAQN：深度自编码器和Q网络 显示英文标题

标题： DAQN: Deep Auto-encoder and Q-Network

摘要： 深度强化学习方法通常需要大量训练图像和执行动作以获得足够的结果。 当在实际环境中使用实际机器人扩展真实任务时，由于输入图像的复杂性或噪声，该方法将需要更多的训练图像，并且在实际机器人上执行大量动作也会成为一个严重的问题。 因此，我们提出了一种扩展的深度强化学习方法，该方法应用生成模型来初始化网络，以减少训练试验的次数。 在本文中，我们使用深度Q网络方法作为深度强化学习方法，使用深度自编码器作为生成模型。 我们在三个不同的任务上进行了实验：一个 carts-pole 游戏，一个 atari 游戏，以及一个使用实际机器人的真实游戏。 与之前的方法相比，所提出的方法在所有任务上都更高效地进行了训练，特别是在包含真实环境图像的任务上快了 2.5 倍。 显示英文摘要

摘要： 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.