Abstract-This paper discusses the effectiveness of deep autoencoder 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.
The similar study with the proposed method is the deep auto-encoder neural networks in reinforcement learning REF .
Deep auto-encoder neural networks in reinforcement learning
The 2010 International Joint Conference on Neural Networks (IJCNN)
The 2010 International Joint Conference on Neural Networks (IJCNN)
Computer Science