Learning and evolution in neural networks
Adaptive Behavior
Evolutionary neurocontrollers for autonomous mobile robots
Neural Networks - Special issue on neural control and robotics: biology and technology
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving Behavioural Choice: An Investigation into Herrnstein's Matching Law
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
The evolution of optimal foraging strategies in populations of digital organisms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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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 well-documented 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.