Deep learning of visual control policies

This paper discusses the effectiveness of deep auto-encoding neural nets in visual reinforcement learning (RL) tasks. We describe a new algorithm and give results on succesfully learning policies directly on synthesized and real images without a predefined image processing. Furthermore, we present a thorough evaluation of the learned feature spaces.