Research in developmental psychology consistently shows that children explore
the world thoroughly and efficiently and that this exploration allows them to
learn. In turn, this early learning supports more robust generalization and
intelligent behavior later in life. While much work has gone into developing
methods for exploration in machine learning, artificial agents have not yet
reached the high standard set by their human counterparts. In this work we
propose using DeepMind Lab (Beattie et al., 2016) as a platform to directly
compare child and agent behaviors and to develop new exploration techniques. We
outline two ongoing experiments to demonstrate the effectiveness of a direct
comparison, and outline a number of open research questions that we believe can
be tested using this methodology.

探索是儿童学习的关键，然而，与人类同行相比，机器学习和人工代理商的探索技术还未达到高标准，该研究提出使用 DeepMind Lab 进行比较，并提出了一些研究问题。

探索探索：在统一环境中比较儿童和强化学习智能体

Exploring Exploration: Comparing Children with RL Agents in Unified  Environments

DeepMind Lab is a first-person 3D game platform designed for research and
development of general artificial intelligence and machine learning systems.
DeepMind Lab can be used to study how autonomous artificial agents may learn
complex tasks in large, partially observed, and visually diverse worlds.
DeepMind Lab has a simple and flexible API enabling creative task-designs and
novel AI-designs to be explored and quickly iterated upon. It is powered by a
fast and widely recognised game engine, and tailored for effective use by the
research community.

DeepMind Lab 是一个可用于研究自主人工智能和机器学习系统的 3D 游戏平台，它具有简单灵活的 API，可用于探索和快速迭代创新任务和 AI 设计。