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.

本文提出了一种基于深度强化学习和注意力机制的多视角环境下的模型，能够学习一个能够根据每个视角的重要性来动态决策的策略，实验结果表明在各种复杂环境中都表现出色。

多视角环境下深度强化学习的演员 - 评论家 - 注意力机制

An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in  Multi-view Environments

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.

研究了多视角环境下的深度强化学习问题，并提出基于注意力机制的方法以学习动态关注环境中不同视角的重要性以促进决策制定和复杂策略的学习。在 TORCS 赛车模拟器和三个其他带有障碍物的复杂 3D 环境上验证了该方法的有效性。