MSC: Text from - izhikevich and ffnn docx

Below is the copy-pasted text from the document "izhikevich and ffnn.docx"

Reason for pasting it here is so that I could delete that doc file and refer to the text here.

Abstract

The Izhikevich spiking neural network model is investigated as a method to develop controllers for a simple, but not trivial, car racing game, called TORCS. The controllers are evolved using the Evolutionary Strategy, and the performance of the best individuals are compared with the hand-coded controller included with the game.

racing competition is a wat wat wat, and since networks of spiking neurons have been gaining popularity in recent years, a great curiosity emerged to want to find out if a spiking neural network could perform as well or better than the other techniques used to train the driver. This paper investigates the performance of the Izhikevich spiking neural network model applied to the TORCS racing game, and trained using Evolutionary Strategy. The evolved driver is compared with the hand-coded driver included in the TORCS championship platform.

Introduction

Many methods have been used to optimize the TORCS game driver for the cig competition, curious if spiking neural network could also perform well in it. Hence, compared the network with the simple driver provided with the competition client.

Results – the spiking neural network does perform better.

Machine learning methods can be used to solve any simulated control problems. In this paper, an evolutionary optimization technique is used to evolve a controller to drive a simulated racing car. The car control is handled by an artificial neural network, using Evolutionary Strategies for the tuning of the weights. Different experiments are run in order to explore combinations of training parameters.