We propose Multi-Task Multi-Behavior MAP-Elites, a variant of MAP-Elites that finds a large number of high-quality solutions for a large set of tasks (optimization problems from a given family). It combines the original MAP-Elites for the search for diversity and Multi-Task MAP-Elites for leveraging similarity between tasks. It performs better than three baselines on a humanoid fault-recovery set of tasks, solving more tasks and finding twice as many solutions per solved task.

翻译：我们提出多任务多行为MAP-Elites，这是MAP-Elites的一种变体，能够针对大量任务（给定问题族中的优化问题）找到大量高质量解。它结合了原始MAP-Elites的多样性搜索机制与多任务MAP-Elites的任务间相似性利用能力。在人体仿生故障恢复任务集上，该方法优于三种基线方法，不仅解决了更多任务，且每个已解决任务的平均解数量翻倍。