How do humans acquire a meaningful understanding of the world with little to no supervision or semantic labels provided by the environment? Here we investigate embodiment with a closed loop between action and perception as one key component in this process. We take a close look at the representations learned by a deep reinforcement learning agent that is trained with high-dimensional visual observations collected in a 3D environment with very sparse rewards. We show that this agent learns stable representations of meaningful concepts such as doors without receiving any semantic labels. Our results show that the agent learns to represent the action relevant information, extracted from a simulated camera stream, in a wide variety of sparse activation patterns. The quality of the representations learned shows the strength of embodied learning and its advantages over fully supervised approaches.

人类如何在很少或没有环境提供的监督或语义标签的情况下获得对世界的有意义的理解？在这里，我们调查在行动和感知之间闭环作为这一过程中的一个关键组成部分的实施例。我们仔细研究了由深度强化学习代理学习的表示形式，该学习代理接受了在3D环境中收集的具有稀疏奖励的高维视觉观察结果的训练。我们证明该代理可以学习有意义的概念（例如门）的稳定表示，而不会收到任何语义标签。我们的结果表明，代理学会了以各种稀疏激活模式来表示从模拟摄像机流中提取的与动作相关的信息。