Evolution of Reinforcement Learning in Uncertain Environments: A Simple Explanation for Complex Foraging Behaviors
Reinforcement learning is a fundamental process by which organisms learn to achieve goals from their interactions with the environment. Using Evolutionary Computation techniques we evolve (near) optimal neuronal learning rules in a simple neural network model of reinforcement learning in bumblebees foraging for nectar. The resulting neural networks exhibit efficient reinforcement learning, allowing the bees to respond rapidly to changes in reward contingencies. The evolved synaptic plasticity dynamics give rise to varying exploration/exploitation levels and to the well-documented choice strategies of risk aversion and probability matching. Risk-averse behavior is evolved even in a risk-less environment, and in contrast to existing theories in economics and game theory, it is shown to be a direct consequence of optimal reinforcement learning, without requiring additional assumptions such as the existence of a non-linear subjective utility function. Our results are corroborated by a rigorous mathematical analysis, and their robustness in real-world situations is supported by experiments in a mobile robot. Thus we provide a biologically founded, parsimonious and novel explanation for risk aversion and probability matching.