# Inducing Functions through Reinforcement Learning without Task Specification

Source: [Inducing Functions through Reinforcement Learning without Task Specification](<https://www.sota2.com/research/paper/68fa6b461e209131cea2d326>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:11:34.522Z

Authors: Junmo Cho, Dong-Hwan Lee, Young-Gyu Yoon

Published: 2021-11-23T04:42:02.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2111.11647>)

[PDF](<https://arxiv.org/pdf/2111.11647.pdf>)