Incremental Neuroevolution of Reactive and Deliberative 3D Agents

Adam Stanton, Alastair Channon

    Research output: Chapter in Book/Published conference outputConference publication


    Following earlier work on the neuroevolution of deliberative behaviour to solve increasingly challenging tasks in a twodimensional dynamic world, this paper presents the results of extending the original system to a three-dimensional rigid
    body simulation. The 3D physically based setting requires that a successful agent continually and deliberately adjust its gait, turning and other motor control over the many stages and sub-stages of these tasks, within its individual evaluation.
    Achieving such complex interplay between motor control and deliberative control, within a neuroevolutionary framework, is the focus of this work. To this end, a novel neural architecture is presented and an incremental evolutionary approach
    used to bootstrap the locomotive behaviour of the agents. Agent morphology is fixed as a quadruped with three degrees of freedom per limb. Agent populations have no initial knowledge of the problem domain, and evolve to move around and then solve progressively more difficult challenges in the environment using a tournament-based co-evolutionary algorithm. The results demonstrate not only success at the tasks but also a variety of intricate lifelike behaviours being
    used, separately and in combination, to achieve this success. Given the problem-agnostic controller architecture, these results indicate a potential for discovering yet more advanced behaviours in yet more complex environments.
    Original languageEnglish
    Title of host publicationAdvances in Artificial Life, ECAL 2015
    PublisherMIT Press Journals
    Number of pages8
    ISBN (Electronic)978-0-262-33027-5-ch063
    Publication statusPublished - 1 Jul 2015

    Bibliographical note

    © 2015 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
    This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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