Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture

Le Goff, Leni; Hart, Emma

Abstract

Algorithmic frameworks for the joint optimisation of a robot's design and controller often utilise a learning loop nested within an evolutionary algorithm to refine the controller associated with a newly generated robot design. Intuitively, it is reasonable to assume that the length of the learning period required is directly related to the complexity of the new design. Therefore, we propose a novel self-adaptive criterion that modifies the learning budget for each individual robot based on setting a target for the progress to be achieved during learning. This stopping criterion can lead Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). to wide variance in learning times per robot evaluated. Research in other domains where variable evaluation time is also observed has suggested that asynchronous architectures are preferable in this situation, leading to improved objective performance and efficiency. We conduct a systematic comparison of synchronous and asynchronous architectures using the new learning stopping criterion in a joint optimisation task, showing that a judicious choice of target learning progress used in conjunction with an asynchronous framework provides considerably better results in terms of fitness and computational efficiency than a synchronous framework-in the latter, the choice of target learning progress has no significant influence. CCS CONCEPTS • Computing methodologies → Continuous space search; Machine learning algorithms.

Citation

Le Goff, L., & Hart, E. (2024, July). Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture. Presented at GECCO 2024 Embodied and Evolved Artificial Intelligence Workshop, Melbourne, Australia

• Presentation Conference Type: Conference Paper (published)
• Conference Name: GECCO 2024 Embodied and Evolved Artificial Intelligence Workshop
• Start Date: Jul 14, 2024
• End Date: Jul 18, 2024
• Acceptance Date: May 3, 2024
• Deposit Date: Jun 4, 2024
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1145/3638530.3664116
• Keywords: Evolutionary Robotics; Morpho-Evolution; Stopping Criteria; Asyn- chronous Evolution
• Publisher URL: https://dl.acm.org/conference/gecco/proceedings