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SUPERIOR UNIVERSITY LAHORE
Faculty of Computer Science & IT
Name: Muhmmad Abdullah
Roll No: Bcsm-F17-093
Section: Bscs-7A
Project Documentation
Flappy Bird AI Final Project
Flappy Bird is a 2013 mobile game, developed by Vietnam-based developer Dong Nguyen and
published by GEARS Studios, a small independent game developer also based in Vietnam. The
game is a side-scroller where the player controls a bird, attempting to fly between rows of green
pipes without hitting them. The objective is to direct a flying bird, named Faby, who moves
continuously to the right, between sets of Mario-like pipes. If the player touches the pipes, they
lose. Faby briefly flaps upward each time that the player taps the screen; if the screen is not
tapped, Faby falls because of gravity; each pair of pipes that he navigates between earns the
player a single point.
THE GOAL:
Using learning algorithms we want to allow AI player to do a training set with different
definitions of states (more details about those differences will discuss later), and learn the flappy
bird world. After the training set we want the player to successfully continue playing as long as
we want without any hit. We can examine the learning success using different parameters as
number of training runs, learning rate, discount factor etc.
Different approaches to state definitions Definition:
GR – grid resolution; parameter we used to quantize the game grid. We used GR values of 1,2
and 4. We discuss more about it in the results part. Naïve state space The various elements in
the state vector have the following ranges:
❖ Vertical: ~ 0 – 500
❖ Horizontal: ~ 0 – 300
❖ Velocity ~ -9 – 9
(units are in pixels and pixels per frame for the velocity) It is easy to see that the size of the state
space is above 10 which means that using all the 6 states data will take too much time to learn,
so we decided to use different approaches of the space quantization.
Features:
 Person vs Person mode
 More obstacles
 More fun and very hard to play
 Enjoy playing the game at home
 No internet Need
 Play Anywhere
 Different types of mode easy hard, Difficult
RESULTS :
The number of options and parameter combinations to run the games is unlimited. Hence we
ran a script that switched between different parameters and state spaces, every 3 minutes. We
chose to present 3 runs for each state space - to show the relation between the parameters and
performance. The graphs show the probability of the bird to crash into a pipe.
Bcsm f17-093(muhammad abdullah)gme programming
Bcsm f17-093(muhammad abdullah)gme programming

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Bcsm f17-093(muhammad abdullah)gme programming

  • 1. SUPERIOR UNIVERSITY LAHORE Faculty of Computer Science & IT Name: Muhmmad Abdullah Roll No: Bcsm-F17-093 Section: Bscs-7A Project Documentation Flappy Bird AI Final Project Flappy Bird is a 2013 mobile game, developed by Vietnam-based developer Dong Nguyen and published by GEARS Studios, a small independent game developer also based in Vietnam. The game is a side-scroller where the player controls a bird, attempting to fly between rows of green pipes without hitting them. The objective is to direct a flying bird, named Faby, who moves continuously to the right, between sets of Mario-like pipes. If the player touches the pipes, they lose. Faby briefly flaps upward each time that the player taps the screen; if the screen is not tapped, Faby falls because of gravity; each pair of pipes that he navigates between earns the player a single point.
  • 2. THE GOAL: Using learning algorithms we want to allow AI player to do a training set with different definitions of states (more details about those differences will discuss later), and learn the flappy bird world. After the training set we want the player to successfully continue playing as long as we want without any hit. We can examine the learning success using different parameters as number of training runs, learning rate, discount factor etc. Different approaches to state definitions Definition: GR – grid resolution; parameter we used to quantize the game grid. We used GR values of 1,2 and 4. We discuss more about it in the results part. Naïve state space The various elements in the state vector have the following ranges: ❖ Vertical: ~ 0 – 500 ❖ Horizontal: ~ 0 – 300 ❖ Velocity ~ -9 – 9 (units are in pixels and pixels per frame for the velocity) It is easy to see that the size of the state space is above 10 which means that using all the 6 states data will take too much time to learn, so we decided to use different approaches of the space quantization. Features:  Person vs Person mode  More obstacles  More fun and very hard to play  Enjoy playing the game at home  No internet Need  Play Anywhere  Different types of mode easy hard, Difficult RESULTS : The number of options and parameter combinations to run the games is unlimited. Hence we ran a script that switched between different parameters and state spaces, every 3 minutes. We chose to present 3 runs for each state space - to show the relation between the parameters and performance. The graphs show the probability of the bird to crash into a pipe.