Evolving Neural Networks in Reinforcement Learning by means of UMDAc

by   Mikel Malagon, et al.

Neural networks are gaining popularity in the reinforcement learning field due to the vast number of successfully solved complex benchmark problems. In fact, artificial intelligence algorithms are, in some cases, able to overcome human professionals. Usually, neural networks have more than a couple of hidden layers, and thus, they involve a large quantity of parameters that need to be optimized. Commonly, numeric approaches are used to optimize the inner parameters of neural networks, such as the stochastic gradient descent. However, these techniques tend to be computationally very expensive, and for some tasks, where effectiveness is crucial, high computational costs are not acceptable. Along these research lines, in this paper we propose to optimize the parameters of neural networks by means of estimation of distribution algorithms. More precisely, the univariate marginal distribution algorithm is used for evolving neural networks in various reinforcement learning tasks. For the sake of validating our idea, we run the proposed algorithm on four OpenAI Gym benchmark problems. In addition, the obtained results were compared with a standard genetic algorithm. Revealing, that optimizing with UMDAc provides better results than the genetic algorithm in most of the cases.


page 1

page 2

page 3

page 4


Hybrid Genetic Algorithm and Hill Climbing Optimization for the Neural Network

In this paper, we propose a hybrid model combining genetic algorithm and...

PathNet: Evolution Channels Gradient Descent in Super Neural Networks

For artificial general intelligence (AGI) it would be efficient if multi...

Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks

Neuroevolution has recently been shown to be quite competitive in reinfo...

Applications of Gaussian Mutation for Self Adaptation in Evolutionary Genetic Algorithms

In recent years, optimization problems have become increasingly more pre...

Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells

This article presents new PEM fuel cell models with hexagonal and pentag...

A hybrid MGA-MSGD ANN training approach for approximate solution of linear elliptic PDEs

We introduce a hybrid "Modified Genetic Algorithm-Multilevel Stochastic ...

Evolving Accuracy: A Genetic Algorithm to Improve Election Night Forecasts

In this paper, we apply genetic algorithms to the field of electoral stu...

Please sign up or login with your details

Forgot password? Click here to reset