Evolution of reinforcement learning in foraging bees: A simple explanation for risk averse behavior

Yael Niv, Daphna Joel, Isaac Meilijson, Eytan Ruppin

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

Reinforcement learning is a fundamental process by which organisms learn to achieve goals from their interactions with the environment. We use evolutionary computation techniques to derive (near-)optimal neuronal learning rules in a simple neural network model of decision-making in simulated bumblebees foraging for nectar. The resulting bees exhibit efficient reinforcement learning. The evolved synaptic plasticity dynamics give rise to varying exploration/exploitation levels and to the well-documented foraging strategy of risk aversion. This behavior is shown to emerge directly from optimal reinforcement learning, providing a biologically founded, parsimonious and novel explanation of risk-averse behavior.

Original languageEnglish (US)
Pages (from-to)951-956
Number of pages6
JournalNeurocomputing
Volume44-46
DOIs
StatePublished - Jul 27 2002
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Keywords

  • Bumble bees
  • Evolutionary computation
  • Exploration/exploitation tradeoff
  • Reinforcement learning
  • Risk aversion

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