Visual Attention Assisted Games

In this work, we propose a committee of attention models developed for improving the deep reinforcement learning frequently used for games. The game environment is manifested with spatial and temporal attention mechanisms so as to focus on important regions while playing the games. We propose that when an agent’s visual attention space is streamlined and strategic temporally coherent representations are used, generalisation will be faster than other traditional architectures. The proposed spatial attention mechanism’s output enables direct analysis of the information recognised by the agent to choose its actions, allowing for a more straightforward interpretation of the provided state. We evaluate several techniques across a variety of games to reinforce our argument. Extensive experimental results on five Atari 2600 games demonstrate that an agent that makes use of this framework is capable of outperforming state-of-the-art models on ATARI tasks while being interpretable.