Instinct and Learning Synergy in Simulated Foraging Using a Neural Network

Instinct and experience are shown to form a potent combination to achieve effective foraging in a simulated environment. A neural network capable of evolving instinct-related neurons and learning from experience is used as the brain of a simple foraging creature that must find food and water in a 3D block world. Instincts provide basic tactics for unsupervised exploration of the world, allowing pathways to food and water to be learned. The combination of both instinct and experience was found to be more effective than either alone. As a comparison, neural network learning also proved superior to Q-Learning on the foraging task.