Tetris – Genetic Algorithm
Presented by ,
Jeethan & Jun
Introduction
 Evolutionary Algorithm
 A commonly used method by which solutions to problems that
might otherwise be impossible to solve are solved.
 Usually be used to find the near-optimal solution for the
problems which have many candidate solutions by using the
evolutionary principles and methods.
 Can also be used to tackle problems that humans don't really
know how to solve.
 Widely used with other algorithms in engineering and other
fields.
Tetris Game
Overview
•
•
•
•
•
•
Tetris is a computer game invented by Alexey Pajitnov in 1985.
Then it was widely played on many other devices such as game consoles.
The game is played on a game-board , usually with a width of 10 and height of
20.
There are seven distinct Tetrominoes , each of which occupies four grid cells
of the game board.
The Tetrominoes move downwards with a certain speed while the player can
rotate them and move them horizontally.
One Tetrominoes stops moving as soon as it hits the ground or previously
placed Tetrominoes.
 Each fully occupied horizontal line in the game-board is removed and all
blocks above slip down by one line.
 The game ends as soon as a new tetrominoes cannot enter the board.
GOAL - The player has to clear as many lines as possible before the board is filled.
Types of Tetrominoes
Rules
 Blocks of four (tetrominoes) falling from the top of
the board. The player moves and rotates the blocks
and stacks them up:
Rules
 black outline is one of the places you can put the
shaped block.
Tetris AI - Outline
 How to design using AI ?
 Using Genetic Algorithm - Determine which positions
are good and which are bad
 The AI is going to go through each position and
choose the best possible one.
Strategies
 Avoid Penalize height since when all the blocks are
stacked up to the top, you lose:
Strategies
 Penalize holes
Blockades
Algorithm for Tetris
Step 1 : Look at the current block and the next block
and simulate ALL possible combinations (positions and
rotations) of the two blocks.
Step 2: Calculate a score for each of the positions.
Step 3: Move the block to the position with the highest
score and repeat.
 To get a score for a position :
Score = A * Sum of Heights
+ B * Number of Clears
+ C * Number of Holes
+ D * Number of Blockades
 For each edge touching , another block
 the wall
 the floor
Implementing genetic algorithm
Charles Darwin specifies four criteria for the process of
natural selection to occur:
 Variation: Organisms in a population must be slightly
different from one another.
 Inheritance: Traits of parent organisms must be passed
onto their offspring.
 Limited space: Only some of the offspring in any
generation is able to survive and pass on its genes.
 Competition: Individuals that are more fit are more likely to
pass on their genes to the next generation.
Factors influencing Natural selection
process
1.
A chromosome which expresses a possible solution to
the problem as a string.
( since there are a set of seven weights ,chromosome is an
array of seven doubles )
2. A fitness function which takes a chromosome as input and
returns a higher value for better solutions .
Fitness Function
 The fitness function – the score is just the number of
lines the AI runs for before it dies.
Scoring system :
 Nintendo’s original scoring system for Tetris — 40
points for one clear, 120 points for
two simultaneous clears, 300 for three simultaneous
clears, and 1200 for four simultaneous clears.
3. A population which is just a set of many
chromosomes
(sixteen chromosomes ; Initially the chromosomes are
filled with randomly generated numbers . Each
generation onwards, the population’s chromosomes
are derived from the best candidates of the previous
generation — but the population size stays the same)
Selection Method
 Tournament selection:
Crossover & Mutation
A crossover operation which determines how parents
combine to produce offspring
• Cross over is a process of taking more than one parent
solutions and producing a child solution from them
A mutation operation which determines how random
deviations manifest themselves
(We have a 10% chance of a mutation – a chromosome that is
different from either parent’s)
Demo
 https://www.youtube.com/watch?v=Q0n1cvNLd04
 https://www.youtube.com/watch?v=WL8hehlRxq0
Conclusion
• Using Genetic Algorithm we can find the local optimum
solution quickly but its very hard to achieve the global
optimum solution – The best solution.
• Suggestion for better performance:
• Adjusting mutation rates with generations
• Instead of a simple weighted approach, add an
intermediary intelligent system and optimize that system.
• E.g. weights of a neural network or parameters of a
fuzzy logic system.