Parametric-Task MAP-Elites.

Optimizing a set of functions simultaneously by leveraging their similarity is called multi-task optimization.Current black-box multitask algorithms only solve a finite set of tasks, even when the tasks originate from a continuous space.In this paper, we introduce Parametric-Task MAP-Elites (PT-ME), a new black-box algorithm for continuous multi-task optimization problems.This algorithm (1) solves a new task at each iteration, effectively covering the continuous space, and (2) exploits a new variation operator based on local linear regression.The resulting dataset of solutions makes it possible to create a function that maps any task parameter to its optimal solution.We show that PT-ME outperforms all baselines, including the deep reinforcement learning algorithm PPO on two parametric-task toy problems and a robotic problem in simulation.