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 boredomas an element of intrinsic motivation. Improvements obtained with the inclusion of this element over classicstrategies are shown through simulations. Boredom is modeled through a chaotic element that generates conditionsfor the creation of routes when the environment does not offer any reward, allowing prompting the robotto navigate. Our proposal seeks to avoid what classical algorithms suffer in scenarios without rewards, generatinglosses of time in the resolution. We demonstrate experimentally that by adding the element of boredomit is possible to generate routes in scenarios in which rewards do not exist, allowing the use of these strategiesin real circumstances and facilitating the robot's navigation towards its objective. The most important contributionsustained by this work corresponds to the fact that it is possible to improve navigation in completelyadverse scenarios for a navigation algorithm based on rewards.