Parametric-Task MAP-Elites

• URL: http://arxiv.org/abs/2402.01275v2
• Date: Thu, 4 Apr 2024 14:21:13 GMT
• Title: Parametric-Task MAP-Elites
• Authors: Timothée Anne, Jean-Baptiste Mouret,
• Abstract summary: Parametric-Task MAP-Elites (PT-ME) is a new black-box algorithm for continuous multi-task optimization problems. 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.
• Score: 3.499042782396682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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