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

Loyola, Oscar; Kern, John; Urrea,Claudio

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.

Más información

Título según WOS: Novel Algorithm for Agent Navigation Based on Intrinsic Motivation Due to Boredom
Título de la Revista: INFORMATION TECHNOLOGY AND CONTROL
Volumen: 50
Número: 3
Editorial: KAUNAS UNIV TECHNOLOGY
Fecha de publicación: 2021
Página de inicio: 485
Página final: 494
DOI:

10.5755/j01.itc.50.3.29242

Notas: ISI