  In reinforcement learning algorithms, it is a common practice to account for
only a single view of the environment to make the desired decisions; however,
utilizing multiple views of the environment can help to promote the learning of
complicated policies. Since the views may frequently suffer from partial
observability, their provided observation can have different levels of
importance. In this paper, we present a novel attention-based deep
reinforcement learning method in a multi-view environment in which each view
can provide various representative information about the environment.
Specifically, our method learns a policy to dynamically attend to views of the
environment based on their importance in the decision-making process. We
evaluate the performance of our method on TORCS racing car simulator and three
other complex 3D environments with obstacles.
