Report
Comparing lifetime learning methods for morphologically evolving robots
| العنوان: | Comparing lifetime learning methods for morphologically evolving robots |
|---|---|
| المؤلفون: | van Diggelen, Fuda, Ferrante, Eliseo, Eiben, A. E. |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Neural and Evolutionary Computing, 68T40 (primary), 68W50 68T05 (Secondary) |
| الوصف: | Evolving morphologies and controllers of robots simultaneously leads to a problem: Even if the parents have well-matching bodies and brains, the stochastic recombination can break this match and cause a body-brain mismatch in their offspring. We argue that this can be mitigated by having newborn robots perform a learning process that optimizes their inherited brain quickly after birth. We compare three different algorithms for doing this. To this end, we consider three algorithmic properties, efficiency, efficacy, and the sensitivity to differences in the morphologies of the robots that run the learning process. Comment: Associated code: https://github.com/fudavd/revolve/tree/learning |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2203.03967 |
| رقم الانضمام: | edsarx.2203.03967 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |