2 research outputs found
Surrogate models for enhancing the efficiency of neuroevolution in reinforcement learning
In the last years, reinforcement learning received a lot of attention. One
method to solve reinforcement learning tasks is Neuroevolution, where neural
networks are optimized by evolutionary algorithms. A disadvantage of
Neuroevolution is that it can require numerous function evaluations, while not
fully utilizing the available information from each fitness evaluation. This is
especially problematic when fitness evaluations become expensive. To reduce the
cost of fitness evaluations, surrogate models can be employed to partially
replace the fitness function. The difficulty of surrogate modeling for
Neuroevolution is the complex search space and how to compare different
networks. To that end, recent studies showed that a kernel based approach,
particular with phenotypic distance measures, works well. These kernels compare
different networks via their behavior (phenotype) rather than their topology or
encoding (genotype). In this work, we discuss the use of surrogate model-based
Neuroevolution (SMB-NE) using a phenotypic distance for reinforcement learning.
In detail, we investigate a) the potential of SMB-NE with respect to evaluation
efficiency and b) how to select adequate input sets for the phenotypic distance
measure in a reinforcement learning problem. The results indicate that we are
able to considerably increase the evaluation efficiency using dynamic input
sets.Comment: This is the authors version of the work. It is posted here for your
personal use. Not for redistribution. The definitive Version of Record was
published in Genetic and Evolutionary Computation Conference (GECCO 2019