SnakeAI Download and Run To run the program you will need Processing YouTube Video Snake Neural Network Each snake contains a neural network. The neural network has an input layer of 24 neurons, 2 hidden layers of 16 neurons, and one output layer of 4 neurons. Note: Network can now be customized with the number of hidden layers as well as the number of neurons in the hidden layers. Vision The snake can see in 8 directions. In each of
these directions the snake looks for 3 things: Distance to food Distance to its own body Distance to a wall 3 x 8 directions = 24 inputs. The 4 outputs are simply the directions the snake can move. Evolution Natural Selection Each generation a population of 2000 snakes is created. For the first generation, all of the neural nets in each of the snakes are initialized randomly. Once the entire population is dead, a fitness score is calculated for each of
the snakes. Using these fitness scores, some of the best snakes are selected to reproduce. In reproduction two snakes are selected and the neural nets of each are crossed and then the resulting child is mutated. This is repeated to create a new population of 2000 new snakes. Fitness A snakes fitness is dependant on how long the snake stays alive as well as its score. However they are not equally important, having a higher score is rewarded more than
a snake who simply stays alive. There is the possibility however that a snake may evolve a strategy where it loops in a certain pattern and never dies. Even though having a high score is prioritized more, if a snake can stay alive forever then that is a clear problem. To avoid this each snake is giving 200 starting moves at the beginning of its life. Every time it eats a piece of food it gains 100 more moves, with
a maximum of 500 moves. This means that snakes who evolve to go in loops will eventually die and snakes who go for the food will not only have a higher score, but stay alive longer. Crossover & Mutation When two snakes are selected for reproduction, what happens is that the snakes brains are crossed with each other. What this means is that part of one parents brain is mixed with part of the second parents and the resulting brain is
assigned to the child. After the crossover the brain is also mutated according to a mutation rate. The mutation rate determines how much of the brain will be randomly altered. Data Collection Graphing The graph represents the score of the best snake from each generations. In some generations the graph may dip bellow the previous, this is because even though the score may have been worse, some trait allowed the snake to live longer and gain a higher overall fitness
. Save & Load Models can be saved and loaded in order to test a model in new situations. The weights for
Frequently asked questions
How many stars and forks are recorded?
greerviau/SnakeAI has 2,389 stars and 556 forks.
What is the repository rank?
greerviau/SnakeAI is ranked 18,704.
What language and category are recorded?
greerviau/SnakeAI has 2,389 stars, uses Processing and belongs to Tutorials.
Which repository is closest in rank?
greerviau/SnakeAI is compared with adithya-s-k/AI-Engineering.academy, ranked 18,705 with 2,383 stars.
Each snake contains a neural network. The neural network has an input layer of 24 neurons, 2 hidden layers of 16 neurons, and one output layer of 4 neurons.
Note: Network can now be customized with the number of hidden layers as well as the number of neurons in the hidden layers.
Vision
The snake can see in 8 directions. In each of these directions the snake looks for 3 things:
Distance to food
Distance to its own body
Distance to a wall
3 x 8 directions = 24 inputs. The 4 outputs are simply the directions the snake can move.
Evolution
Natural Selection
Each generation a population of 2000 snakes is created. For the first generation, all of the neural nets in each of the snakes are initialized randomly. Once the entire population is dead, a fitness score is calculated for each of the snakes. Using these fitness scores, some of the best snakes are selected to reproduce. In reproduction two snakes are selected and the neural nets of each are crossed and then the resulting child is mutated. This is repeated to create a new population of 2000 new snakes.
Fitness
A snakes fitness is dependant on how long the snake stays alive as well as its score. However they are not equally important, having a higher score is rewarded more than a snake who simply stays alive. There is the possibility however that a snake may evolve a strategy where it loops in a certain pattern and never dies. Even though having a high score is prioritized more, if a snake can stay alive forever then that is a clear problem. To avoid this each snake is giving 200 starting moves at the beginning of its life. Every time it eats a piece of food it gains 100 more moves, with a maximum of 500 moves. This means that snakes who evolve to go in loops will eventually die and snakes who go for the food will not only have a higher score, but stay alive longer.
Crossover & Mutation
When two snakes are selected for reproduction, what happens is that the snakes brains are crossed with each other. What this means is that part of one parents brain is mixed with part of the second parents and the resulting brain is assigned to the child. After the crossover the brain is also mutated according to a mutation rate. The mutation rate determines how much of the brain will be randomly altered.
Data Collection
Graphing
The graph represents the score of the best snake from each generations. In some generations the graph may dip bellow the previous, this is because even though the score may have been worse, some trait allowed the snake to live longer and gain a higher overall fitness.
Save & Load
Models can be saved and loaded in order to test a model in new situations. The weights for each connection are saved in a CSV file. The evolution graph is also saved in order to view the evolution progress of the model.
