We present an approach for agents to learn representations of a global map
from sensor data, to aid their exploration in new environments. To achieve
this, we embed procedures mimicking that of traditional Simultaneous
Localization and Mapping (SLAM) into the soft attention based addressing of
external memory architectures, in which the external memory acts as an internal
representation of the environment. This structure encourages the evolution of
SLAM-like behaviors inside a completely differentiable deep neural network. We
show that this approach can help reinforcement learning agents to successfully
explore new environments where long-term memory is essential. We validate our
approach in both challenging grid-world environments and preliminary Gazebo
experiments. A video of our experiments can be found at: this https URL

本研究提出了一种新的方法，通过嵌入传统同时定位与建图（SLAM）的程序到软的注意力机制中，从而使得机器代理从传感器数据学习全局地图的表征，在无人环境中，可以通过深度神经网络进化具有类似 SLAM 的行为和强化学习代理，成功地在必须保持长期记忆的新环境中进行探索。