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.
The similar study with the proposed method is the deep auto-encoder neural networks in reinforcement learning @cite_14 . This method first trains features by the auto-encoder, then trains policies by the batch-mode reinforcement learning algorithm. The architecture of this method is similar to ours; however, the motivation is different. They used the auto-encoder for reducing the dimension of input for the reinforcement learning. We used the auto-encoder to reduce the number of training trials for the reinforcement learning phase. Hence, we discussed the training efficiency in this paper. Furthermore, we used the latest convolutional auto-encoder and the latest deep reinforcement learning method.
@cite_14
2154997814
This paper discusses the effectiveness of deep auto-encoder neural networks in visual reinforcement learning (RL) tasks. We propose a framework for combining the training of deep auto-encoders (for learning compact feature spaces) with recently-proposed batch-mode RL algorithms (for learning policies). An emphasis is put on the data-efficiency of this combination and on studying the properties of the feature spaces automatically constructed by the deep auto-encoders. These feature spaces are empirically shown to adequately resemble existing similarities and spatial relations between observations and allow to learn useful policies. We propose several methods for improving the topology of the feature spaces making use of task-dependent information. Finally, we present first results on successfully learning good control policies directly on synthesized and real images.