Abstract
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.
Original language | English |
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Pages (from-to) | 951-956 |
Number of pages | 6 |
Journal | Neurocomputing |
Volume | 44-46 |
DOIs | |
State | Published - 2002 |
Keywords
- Bumble bees
- Evolutionary computation
- Exploration/exploitation tradeoff
- Reinforcement learning
- Risk aversion