Merge commit 'ab0c8b85212c59a9a97a09180eefcaa2b0b5d080'

genetic_alg.py

@@ -5,11 +5,13 @@ import numpy as np
 class NeuralNetwork(nn.Module):
     def __init__(self, inputSize):
         super().__init__()
-        self.hidden = nn.Linear(inputSize, 32)
-        self.output = nn.Linear(32, 3)
+        self.hidden1 = nn.Linear(inputSize, 32)
+        self.hidden2 = nn.Linear(32, 16)
+        self.output = nn.Linear(16, 3)
 
     def forward(self, x):
-        x = torch.relu(self.hidden(x))
+        x = torch.relu(self.hidden1(x))
+        x = torch.relu(self.hidden2(x))
         x = self.output(x)
         return torch.argmax(x)
 
@@ -28,9 +30,12 @@ class GeneticAlgorithm:
     def crossover(self, parent1: NeuralNetwork, parent2: NeuralNetwork):
         child1 = NeuralNetwork(self.inputSize).to(self.device)
         child2 = NeuralNetwork(self.inputSize).to(self.device)
-        point = len(child1.hidden.weight.data) // 2
-        child1.hidden.weight.data = torch.cat((parent1.hidden.weight.data[:point], parent2.hidden.weight.data[point:]), dim=0)
-        child2.hidden.weight.data = torch.cat((parent2.hidden.weight.data[:point], parent1.hidden.weight.data[point:]), dim=0)
+        point1 = len(child1.hidden1.weight.data) // 2
+        point2 = len(child1.hidden2.weight.data) // 2
+        child1.hidden1.weight.data = torch.cat((parent1.hidden1.weight.data[:point1], parent2.hidden1.weight.data[point1:]), dim=0)
+        child2.hidden1.weight.data = torch.cat((parent2.hidden1.weight.data[:point1], parent1.hidden1.weight.data[point1:]), dim=0)
+        child1.hidden2.weight.data = torch.cat((parent1.hidden2.weight.data[:point2], parent2.hidden2.weight.data[point2:]), dim=0)
+        child2.hidden2.weight.data = torch.cat((parent2.hidden2.weight.data[:point2], parent1.hidden2.weight.data[point2:]), dim=0)
         child1.output.weight.data = parent1.output.weight.data.clone().detach()
         child2.output.weight.data = parent2.output.weight.data.clone().detach()
         return child1, child2

main.py

@@ -7,6 +7,7 @@ POPULATION_SIZE = 20
 MUTATION_RATE = 0.5
 POPULATION_BEST = 0.2
 FRAME_RATE = 160 #TODO: FIX THE INCORRECT FRAME RATE CORRELATION
+BARRIER_SPEED = 10
 
 pygame.init()
 
@@ -32,8 +33,6 @@ def init_game():
     grass = Background("images/Grass.jpg", 0, 0)
     grass.set_size(WIDTH, HEIGHT)
 
-    BARRIER_SPEED = 15
-
     gameobjects = []
     
     barriers = []
@@ -65,12 +64,12 @@ def get_features(horse):
                last_barrier.rect.bottomright[0], last_barrier.rect.bottomright[1], 
                horse.rect.topleft[0], horse.rect.topleft[1],
                horse.rect.bottomright[0], horse.rect.bottomright[1],
-               HEIGHT, BARRIER_SPEED]
+               0, HEIGHT, BARRIER_SPEED]
     return features 
 
-genecticAlg = GeneticAlgorithm(POPULATION_SIZE, MUTATION_RATE, POPULATION_BEST, 10)
 horses = [Horse("images/Horse_1.png", 50, HEIGHT/2 + 0 * i) for i in range(POPULATION_SIZE)]
 init_game()
+genecticAlg = GeneticAlgorithm(POPULATION_SIZE, MUTATION_RATE, POPULATION_BEST, len(get_features(horses[0])))
 while True:
     for event in pygame.event.get():
         if event.type == pygame.QUIT:  # Handle window close event