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.
翻译:强化学习的深层次学习方法导致一个普通的学习者能够接受视觉输入培训,在人和超人层面玩各种街机游戏。谷歌深明队的创造者们称之为:深Q网络(DQN) 。我们通过“软”和“硬”关注机制展示了DQN的延伸。在多个Atari 2600游戏上提议的深视经常Q网络算法(DARQN)的测试显示了优于DQN的性能水平。此外,内部关注机制通过强调代理人在决策时所关注的游戏屏幕区域,使得培训过程能够直接在线监测。