1 research outputs found
Novelty-organizing team of classifiers in noisy and dynamic environments
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