In this work we use deep reinforcement learning to create an autonomous agent that can navigate in a two-dimensional space using only raw auditory sensory information from the environment, a problem that has received very little attention in the reinforcement learning literature. Our experiments show that the agent can successfully identify a particular target speaker among a set of $N$ predefined speakers in a room and move itself towards that speaker, while avoiding collision with other speakers or going outside the room boundaries. The agent is shown to be robust to speaker pitch shifting and it can learn to navigate the environment, even when a limited number of training utterances are available for each speaker.

翻译：在这项工作中,我们利用深层强化学习创造一个自主的代理器,它只能使用来自环境的原始听觉信息在二维空间导航,这个问题在强化学习文献中很少引起注意。我们的实验表明,该代理器可以成功地在会议室内一组预先界定的发言者中确定一个特定的目标演讲人,然后转向该演讲人,同时避免与其他发言者发生碰撞或走出会议室的边界。该代理器被证明对扬声器的移动非常强大,它可以学会在环境中导航,即使每个发言者都可获得数量有限的培训话语。