# Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning

## TL;DR

This paper explores the use of surrogate models with phenotypic distance measures to improve the efficiency of neuroevolution in reinforcement learning, reducing costly fitness evaluations.

## Contribution

It demonstrates how surrogate model-based neuroevolution with dynamic input sets can significantly enhance evaluation efficiency in reinforcement learning tasks.

## Key findings

- Evaluation efficiency increased with dynamic input sets
- Phenotypic distance measures effectively compare neural networks
- Surrogate models reduce the number of expensive fitness evaluations

## Abstract

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.