  A deep learning approach to reinforcement learning led to a general learner
able to train on visual input to play a variety of arcade games at the human
and superhuman levels. Its creators at the Google DeepMind's team called the
approach: Deep Q-Network (DQN). We present an extension of DQN by "soft" and
"hard" attention mechanisms. Tests of the proposed Deep Attention Recurrent
Q-Network (DARQN) algorithm on multiple Atari 2600 games show level of
performance superior to that of DQN. Moreover, built-in attention mechanisms
allow a direct online monitoring of the training process by highlighting the
regions of the game screen the agent is focusing on when making decisions.
