  In reinforcement learning algorithms, leveraging multiple views of the
environment can improve the learning of complicated policies. In multi-view
environments, due to the fact that the views may frequently suffer from partial
observability, their level of importance are often different. In this paper, we
propose a deep reinforcement learning method and an attention mechanism in a
multi-view environment. Each view can provide various representative
information about the environment. Through our attention mechanism, our method
generates a single feature representation of environment given its multiple
views. It learns a policy to dynamically attend to each view based on its
importance in the decision-making process. Through experiments, we show that
our method outperforms its state-of-the-art baselines on TORCS racing car
simulator and three other complex 3D environments with obstacles. We also
provide experimental results to evaluate the performance of our method on noisy
conditions and partial observation settings.
