Thaen/DynamicAbstractionSystem
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Adjust grid dimensions and refine cell mutation parameters for improved simulation behavior
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Sam 2025-06-25 00:30:05 -05:00
parent 4e90ecb885
commit 8bb5c3edfc
3 changed files with 8 additions and 8 deletions
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4
config/constants.py
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@ -19,8 +19,8 @@ SELECTION_GRAY = (128, 128, 128, 80)
SELECTION_BORDER = (80, 80, 90)
# Grid settings
GRID_WIDTH = 100
GRID_HEIGHT = 100
GRID_WIDTH = 50
GRID_HEIGHT = 50
CELL_SIZE = 20
RENDER_BUFFER = 50

6
core/simulation_engine.py
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@ -81,13 +81,13 @@ class SimulationEngine:
if FOOD_SPAWNING:
for _ in range(FOOD_OBJECTS_COUNT):
x = random.randint(-half_width, half_width)
y = random.randint(-half_height, half_height)
x = random.randint(-half_width // 2, half_width // 2)
y = random.randint(-half_height // 2, half_height // 2)
world.add_object(FoodObject(Position(x=x, y=y)))
for _ in range(300):
new_cell = DefaultCell(
Position(x=random.randint(-half_width, half_width), y=random.randint(-half_height, half_height)),
Position(x=random.randint(-half_width // 2, half_width // 2), y=random.randint(-half_height // 2, half_height // 2)),
Rotation(angle=0)
)
new_cell.behavioral_model = new_cell.behavioral_model.mutate(3)

6
world/objects.py
View File

@ -315,10 +315,10 @@ class DefaultCell(BaseEntity):
duplicate_y_2 += random.randint(-self.max_visual_width, self.max_visual_width)
new_cell = DefaultCell(Position(x=int(duplicate_x), y=int(duplicate_y)), Rotation(angle=random.randint(0, 359)))
new_cell.set_brain(self.behavioral_model.mutate(0.4))
new_cell.set_brain(self.behavioral_model.mutate(0.025))
new_cell_2 = DefaultCell(Position(x=int(duplicate_x_2), y=int(duplicate_y_2)), Rotation(angle=random.randint(0, 359)))
new_cell_2.set_brain(self.behavioral_model.mutate(0.4))
new_cell_2.set_brain(self.behavioral_model.mutate(0.025))
return [new_cell, new_cell_2]
@ -350,7 +350,7 @@ class DefaultCell(BaseEntity):
movement_cost = abs(output_data["angular_acceleration"]) + abs(output_data["linear_acceleration"])
self.energy -= (self.behavioral_model.neural_network.network_cost * 0.1) + 1 + (0.3 * movement_cost)
self.energy -= (self.behavioral_model.neural_network.network_cost * 0.15) + 1 + (0.3 * movement_cost)
return self