Novel Algorithm for Agent Navigation Based on Intrinsic Motivation Due to Boredom

Abstract

We propose a novel algorithm for the navigation of agents based on reinforcement learning, using boredom
as an element of intrinsic motivation. Improvements obtained with the inclusion of this element over classic
strategies are shown through simulations. Boredom is modeled through a chaotic element that generates conditions
for the creation of routes when the environment does not offer any reward, allowing prompting the robot
to navigate. Our proposal seeks to avoid what classical algorithms suffer in scenarios without rewards, generating
losses of time in the resolution. We demonstrate experimentally that by adding the element of boredom
it is possible to generate routes in scenarios in which rewards do not exist, allowing the use of these strategies
in real circumstances and facilitating the robot's navigation towards its objective. The most important contribution
sustained by this work corresponds to the fact that it is possible to improve navigation in completely
adverse scenarios for a navigation algorithm based on rewards.