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arXiv · 2402.01275

Parametric-Task MAP-Elites

Timothée Anne·Jean-Baptiste Mouret
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Abstract

Optimizing a set of functions simultaneously by leveraging their similarity is called multi-task optimization. Current black-box multi-task 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.

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Timothée Anne, Jean-Baptiste Mouret. 2024-02-02. Parametric-Task MAP-Elites. https://doi.org/10.1145/3638529.3653993

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