Novelty-organizing team of classifiers in noisy and dynamic environments

TL;DR

This paper introduces NOTC, a novel classifier team algorithm that effectively handles noisy and changing environments, outperforming NEAT in continuous control tasks by dividing input space for easier problem solving.

Contribution

The paper presents NOTC, a new classifier team approach that adapts to dynamic environments and demonstrates superior performance over NEAT in noisy and unstable control problems.

Findings

NOTC outperforms NEAT in noisy and dynamic mountain car tasks.

NOTC's input space division improves problem difficulty management.

NEAT converges faster but with lower overall performance.

Abstract

In the real world, the environment is constantly changing with the input variables under the effect of noise. However, few algorithms were shown to be able to work under those circumstances. Here, Novelty-Organizing Team of Classifiers (NOTC) is applied to the continuous action mountain car as well as two variations of it: a noisy mountain car and an unstable weather mountain car. These problems take respectively noise and change of problem dynamics into account. Moreover, NOTC is compared with NeuroEvolution of Augmenting Topologies (NEAT) in these problems, revealing a trade-off between the approaches. While NOTC achieves the best performance in all of the problems, NEAT needs less trials to converge. It is demonstrated that NOTC achieves better performance because of its division of the input space (creating easier problems). Unfortunately, this division of input space also requires a bit of time to bootstrap.