Parametric-Task MAP-Elites
<|reference_start|>Parametric-Task MAP-Elites: 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.<|reference_end|>
@article{anne2024parametric-task,
    title={Parametric-Task MAP-Elites},
    author={Timoth'ee Anne, Jean-Baptiste Mouret},
    journal={arXiv preprint arXiv:2402.01275},
    year={2024},
    doi={10.1145/3638529.3653993},
    archivePrefix={arXiv},
    eprint={2402.01275},
    primaryClass={cs.NE cs.LG}
}