In real-world environments, robots need to be resilient to damages and robust
to unforeseen scenarios. Quality-Diversity (QD) algorithms have been
successfully used to make robots adapt to damages in seconds by leveraging a
diverse set of learned skills. A high diversity of skills increases the chances
of a robot to succeed at overcoming new situations since there are more
potential alternatives to solve a new task.However, finding and storing a large
behavioural diversity of multiple skills often leads to an increase in
computational complexity. Furthermore, robot planning in a large skill space is
an additional challenge that arises with an increased number of skills.
Hierarchical structures can help reducing this search and storage complexity by
breaking down skills into primitive skills. In this paper, we introduce the
Hierarchical Trial and Error algorithm, which uses a hierarchical behavioural
repertoire to learn diverse skills and leverages them to make the robot adapt
quickly in the physical world. We show that the hierarchical decomposition of
skills enables the robot to learn more complex behaviours while keeping the
learning of the repertoire tractable. Experiments with a hexapod robot show
that our method solves a maze navigation tasks with 20% less actions in
simulation, and 43% less actions in the physical world, for the most
challenging scenarios than the best baselines while having 78% less complete
failures.

使用 Hierarchical Trial and Error 算法，将 QD 算法分层结构化以增加机器人学习技能的多样性，并使机器人能够更快地适应物理世界，实验表明，该方法优于现有基准算法，可提高机器人迎接新挑战的能力。

具有分层品质多样性的物理机器人在线损伤恢复

Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity

In this paper, we present a method for automated persona-driven video game
tutorial level generation. Tutorial levels are scenarios in which the player
can explore and discover different rules and game mechanics. Procedural
personas can guide generators to create content which encourages or discourages
certain playstyle behaviors. In this system, we use procedural personas to
calculate the behavioral characteristics of levels which are evolved using the
quality-diversity algorithm known as Constrained MAP-Elites. An evolved map's
quality is determined by its simplicity: the simpler it is, the better it is.
Within this work, we show that the generated maps can strongly encourage or
discourage different persona-like behaviors and range from simple solutions to
complex puzzle-levels, making them perfect candidates for a tutorial generative
system.

该研究提出了一种基于自动化角色驱动的方法来生成视频游戏教程级别的系统，使用 ' 约束 MAP-Elites' 算法和程序员确定行为特征来创造出一个带有不同行为倾向的级别，并证明了其可行性。