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

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 condi-tions 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 contri-bution 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. © 2021, Kauno Technologijos Universitetas. All rights reserved.