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

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 bumbleb...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2002-06, Vol.44, p.951-956
Main Authors: Niv, Yael, Joel, Daphna, Meilijson, Isaac, Ruppin, Eytan
Format: Article
Language:English
Subjects:
Bumble bees
Evolutionary computation
Exploration/exploitation tradeoff
Reinforcement learning
Risk aversion
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/S0925-2312(02)00496-4