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