Inducing Functions through Reinforcement Learning without Task Specification

Understand

We report a bio-inspired framework for training a neural network through reinforcement learning to induce high level functions within the network.

• Based on the interpretation that animals have gained their cognitive functions such as object recognition - without ever being specifically trained for - as a result of maximizing their fitness to the environment, we place our agent in an environment where developing certain functions may facilitate decision making.
• The experimental results show that high level functions, such as image classification and hidden variable estimation, can be naturally and simultaneously induced without any pre-training or specifying them.