Sciweavers

Share
ECAL
2001
Springer

Evolution of Reinforcement Learning in Uncertain Environments: Emergence of Risk-Aversion and Matching

9 years 11 months ago
Evolution of Reinforcement Learning in Uncertain Environments: Emergence of Risk-Aversion and Matching
Reinforcement learning (RL) is a fundamental process by which organisms learn to achieve a goal from interactions with the environment. Using Artificial Life techniques we derive (near-)optimal neuronal learning rules in a simple neural network model of decision-making in simulated bumblebees foraging for nectar. The resulting networks exhibit efficient RL, allowing the bees to respond rapidly to changes in reward contingencies. The evolved synaptic plasticity dynamics give rise to varying exploration/exploitation levels from which emerge the welldocumented foraging strategies of risk aversion and probability matching. These are shown to be a direct result of optimal RL, providing a biologically founded, parsimonious and novel explanation for these behaviors. Our results are corroborated by a rigorous mathematical analysis and by experiments in mobile robots.
Yael Niv, Daphna Joel, Isaac Meilijson, Eytan Rupp
Added 28 Jul 2010
Updated 28 Jul 2010
Type Conference
Year 2001
Where ECAL
Authors Yael Niv, Daphna Joel, Isaac Meilijson, Eytan Ruppin
Comments (0)
books