Humans construct internal cognitive maps of their environment directly from
sensory inputs without access to a system of explicit coordinates or distance
measurements. While machine learning algorithms like SLAM utilize specialized
visual inference procedures to identify visual features and construct spatial
maps from visual and odometry data, the general nature of cognitive maps in the
brain suggests a unified mapping algorithmic strategy that can generalize to
auditory, tactile, and linguistic inputs. Here, we demonstrate that predictive
coding provides a natural and versatile neural network algorithm for
constructing spatial maps using sensory data. We introduce a framework in which
an agent navigates a virtual environment while engaging in visual predictive
coding using a self-attention-equipped convolutional neural network. While
learning a next image prediction task, the agent automatically constructs an
internal representation of the environment that quantitatively reflects
distances. The internal map enables the agent to pinpoint its location relative
to landmarks using only visual information.The predictive coding network
generates a vectorized encoding of the environment that supports vector
navigation where individual latent space units delineate localized, overlapping
neighborhoods in the environment. Broadly, our work introduces predictive
coding as a unified algorithmic framework for constructing cognitive maps that
can naturally extend to the mapping of auditory, sensorimotor, and linguistic
inputs.

预测编码为构建认知地图的统一算法框架，并能通过视觉信息定位环境中的位置。

虚拟环境的自动映射与视觉预测编码

Automated mapping of virtual environments with visual predictive coding

This paper considers the problem of efficient exploration of unseen
environments, a key challenge in AI. We propose a `learning to explore'
framework where we learn a policy from a distribution of environments. At test
time, presented with an unseen environment from the same distribution, the
policy aims to generalize the exploration strategy to visit the maximum number
of unique states in a limited number of steps. We particularly focus on
environments with graph-structured state-spaces that are encountered in many
important real-world applications like software testing and map building. We
formulate this task as a reinforcement learning problem where the `exploration'
agent is rewarded for transitioning to previously unseen environment states and
employ a graph-structured memory to encode the agent's past trajectory.
Experimental results demonstrate that our approach is extremely effective for
exploration of spatial maps; and when applied on the challenging problems of
coverage-guided software-testing of domain-specific programs and real-world
mobile applications, it outperforms methods that have been hand-engineered by
human experts.

该研究考虑了人工智能中尚未开发过的环境在探索方面的问题，并提出了一种从环境分布中学习策略的方法，将其作为强化学习任务来处理，以期能够在最短的步数内访问尽可能多的独特状态。实验结果表明，该方法在空间地图探索和领域特定程序和现实世界移动应用的覆盖率导向软件测试方面表现出色。