Teaching Computer to Play games by using Neural

Network & Genetic Algorithms

Problem Statement

This project explores the application of

several machine learning techniques such

as Artificial Neural Network & Genetic

Algorithm to develop an agent capable of

successfully playing Super Mario Bros by

itself. The game presents a partially

observable, episodic problem & thus

provides an interesting and applicable

platform to explore the power of mentioned

machine learning techniques.

Related Work

There are several Neuro-evolution methods

out there. In the scope of this project, we

discover and follow a method of NEAT. It

follows genetic algorithm and tracks genes

with historical markings to allow crossover

between different topologies, protects

innovation via speciation.

Solution/Experiment/Design

Fig1: A training picture of Mario Bros„

Artificial neural network is implemented and

trained by using genetic algorithm to take on

this problem. We designed the fitness

function ourselves and let the agent run to

measure the max fitness it can achieve.

Implementation

The agent is developed by using Lua

Programming Language on BizHawk

emulator and Super Mario Bros (Japanese

edition). We implement a Neural Network

which takes the input as all the sprites in the

screen and train it as well as make it evolve

by using Genetic algorithm and Neuro-

evolution of Augmenting Topologies (NEAT).

The output will be the set of controlling key for

playing the game.

Evaluation/Discussion

While the objective for the competitors was to

design or learn a controller that played infinite

mario bros as well as possible, the objective

for the organizers is to organize a competition

that accurately tested the efficacy of various

controller representations and learning

algorithms for controlling an agent in a

platform game.

• Complexifying function makes NEAT

unique among GA„s

Outlook

In the future, a new fitness function may

come in and may give a better result for this

game. With some effort of modifying into the

functionality of game.

References [1] A.Ismail , N.A.El_Ramly “Game Theory Using Genetic

Algorithms”http://www.iaeng.org/publication/WCE2007/WC

E2007_pp61-64.pdf

[2] Kenneth O. Stanley “Evolving Neural Networks through

Augmenting Topologies”

http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf

MUHAMMAD AHSAN NAWAZ (ahsanntu@hotmail.com),

VU THANH NGO (ntvu02@gmail.com)&

JUNAID ASGHAR (junaidasghar160@gmail.com)

Winter 2015/2016

Information Engineering and Computer Science, M. Sc.

Applied Research Project