DAQN: Deep Auto-encoder and Q-Network
This addresses the challenge of reducing training trials for deep reinforcement learning in real-world robotics, though it appears incremental as it combines existing methods.
The authors tackled the problem of deep reinforcement learning requiring many training images and actions, especially in real-world robot tasks, by proposing a method that uses a deep auto-encoder to initialize the network, resulting in 2.5 times faster training on real environment images.
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.