Behavioural Plasticity Can Help Evolving Agents in Dynamic Environments but at the Cost of Volatility

Chloe M. Barnes, Anikó Ekárt, Kai Olav Ellefsen, Kyrre Glette, Peter R. Lewis, Jim Tørresen

Research output: Contribution to journalArticlepeer-review

Abstract

Neural networks have been widely used in agent learning architectures; however, learnings for one task might nullify learnings for another. Behavioural plasticity enables humans and animals alike to respond to environmental changes without degrading learned knowledge; this can be achieved by regulating behaviour with neuromodulation—a biological process found in the brain. We demonstrate that by modulating activity-propagating signals, neurally trained agents evolving to solve tasks in dynamic environments that are prone to change can expect a significantly higher fitness than non-modulatory agents and also achieve their goals more often. Further, we show that while behavioural plasticity can help agents to achieve goals in these variable environments, this ability to overcome environmental changes with greater success comes at the cost of highly volatile evolution.
Original languageEnglish
Article number11
Pages (from-to)1-26
Number of pages26
JournalACM Transactions on Autonomous and Adaptive Systems
Volume15
Issue number4
DOIs
Publication statusPublished - 20 Dec 2021

Bibliographical note

© 2021 Copyright held by the owner/author(s)

Keywords

  • Software
  • Computer Science (miscellaneous)
  • Control and Systems Engineering

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