  Deep reinforcement learning has become popular over recent years, showing
superiority on different visual-input tasks such as playing Atari games and
robot navigation. Although objects are important image elements, few work
considers enhancing deep reinforcement learning with object characteristics. In
this paper, we propose a novel method that can incorporate object recognition
processing to deep reinforcement learning models. This approach can be adapted
to any existing deep reinforcement learning frameworks. State-of-the-art
results are shown in experiments on Atari games. We also propose a new approach
called "object saliency maps" to visually explain the actions made by deep
reinforcement learning agents.
