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Merged
SzeMrok merged 1 commit into from
Jan 12, 2026
Merged

Add agent mating #24

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9 changes: 8 additions & 1 deletion config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -7,4 +7,11 @@ panel:
x: 50
y: 50
grid:
cell_size: 25
cell_size: 25
mating:
min_age_percent: 0.2
min_energy_level: 0.75
max_range: 5
mutation_chance: 0.05
mutation_multiply_border: 0.2
mutation_addding_border: 0.05
101 changes: 11 additions & 90 deletions src/agent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,28 +10,6 @@
from environment import Environment
import uuid

def _clamp(x: float, lo: float, hi: float) -> float:
if x < lo:
return lo
if x > hi:
return hi
return x


def _tanh(x: float) -> float:
return math.tanh(x)


def _sqrt_scale(points: float, k: float) -> float:
return k * math.sqrt(max(0.0, points))


def _dist2(ax: float, ay: float, bx: float, by: float) -> float:
dx = ax - bx
dy = ay - by
return dx * dx + dy * dy



def _clamp(x: float, lo: float, hi: float) -> float:
"""Clamps value x between lo and hi."""
Expand All @@ -52,8 +30,8 @@ def _sqrt_scale(points: float, k: float) -> float:
return k * math.sqrt(max(0.0, points))


def _dist2(ax: float, ay: float, bx: float, by: float) -> float:
"""Returns squared Euclidean distance between (ax, ay) and (bx, by)."""
def dist2(ax: float, ay: float, bx: float, by: float) -> float:
"""Returns squared Euclidean dist2ance between (ax, ay) and (bx, by)."""
dx = ax - bx
dy = ay - by
return dx * dx + dy * dy
Expand All @@ -70,12 +48,12 @@ class Agent:
# 4 y_n -> y position normalized to world height (0..1)
# 5 head_x -> facing direction x (cos(angle))
# 6 head_y -> facing direction y (-sin(angle))
# 7 food_d_n -> distance to nearest food normalized by sight (0..1)
# 7 food_d_n -> dist2ance to nearest food normalized by sight (0..1)
# 8 food_dx_n -> x direction to nearest food (normalized)
# 9 food_dy_n -> y direction to nearest food (normalized)
# 10 friend_count_n -> nearby friends count (normalized)
# 11 enemy_count_n -> nearby enemies count (normalized)
# 12 enemy_d_n -> distance to nearest enemy normalized by sight (0..1)
# 12 enemy_d_n -> dist2ance to nearest enemy normalized by sight (0..1)
# 13 enemy_dx_n -> x direction to nearest enemy (normalized)
# 14 enemy_dy_n -> y direction to nearest enemy (normalized)
# 15 bias -> constant bias input (always 1.0)
Expand All @@ -92,17 +70,14 @@ class Agent:

body_points_total = 100

distance_to_partner_to_mate = 0.05
#parameters to reproduction
chance_for_mutation = 0.05
mutation_multiply_border = 0.2 # random.uniform(1-var,1+var)
mutation_addding_border = 0.05

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()

def __init__(self, position: tuple, environment : Environment,/, decision_matrix : typing.List[typing.List[int]] = None, genome = None ):
from mating import Mating

self.environment = environment
self.mate_module :Mating = Mating(self)
self.x = float(position[0])
self.y = float(position[1])

Expand Down Expand Up @@ -145,7 +120,7 @@ def __init__(self, position: tuple, environment : Environment,/, decision_matrix
self._last_food = None

def _random_body_points(self, total: int):
"""Randomly distribute total points across body stat keys."""
"""Randomly dist2ribute total points across body stat keys."""
keys = ["hp", "energy", "speed", "attack", "lifespan", "sight", "agility"]
pts = {k: 0 for k in keys}
for _ in range(total):
Expand Down Expand Up @@ -188,7 +163,7 @@ def sense(self, foods=None, agents=None):
for f in foods:
fx = float(getattr(f, "x", 0.0))
fy = float(getattr(f, "y", 0.0))
d2 = _dist2(self.x, self.y, fx, fy)
d2 = dist2(self.x, self.y, fx, fy)
if d2 < best_d2:
best_d2 = d2
best = (fx, fy)
Expand Down Expand Up @@ -222,7 +197,7 @@ def sense(self, foods=None, agents=None):
continue
ax = float(getattr(a, "x", 0.0))
ay = float(getattr(a, "y", 0.0))
d2 = _dist2(self.x, self.y, ax, ay)
d2 = dist2(self.x, self.y, ax, ay)
if d2 > r2:
continue

Expand Down Expand Up @@ -370,16 +345,7 @@ def _attack(self):
self.energy = max(0.0, self.energy - 0.08)

def _mate(self):
from environment import Environment
self.energy = max(0.0, self.energy - 0.3)
agents : typing.List[Agent] = self.environment.get_agents()
close_agents : typing.List[Agent] = []
for ag in agents:
if _dist2(ag.x, ag.y, self.x, self.y) < self.distance_to_partner_to_mate:
close_agents.append(ag)
if len(close_agents) > 0:
matrix, vector = self.create_new_genes( random.choice(close_agents))
self.environment.create_agent(Agent((self.x, self.y), self.environment, decision_matrix = matrix, genome = vector))
self.mate_module.mate()


def _tick_body(self):
Expand Down Expand Up @@ -424,51 +390,6 @@ def update(self, speed_modifier):

self._tick_body()

def create_new_genes(self, second : Agent):
matrix = self.create_new_decision_matrix(second)
vector = self.create_new_genome_vector(second)

numbers_to_change_in_matrix = int(len(matrix) * len(matrix[0]) / (1 / self.chance_for_mutation))

#Applying mutation - there could be multiply of value or adding

for _ in range(numbers_to_change_in_matrix):
row = random.choice(matrix)
i = random.randint(0, len(row)-1)
if random.random() < 0.2:
row[i] *= random.uniform(1 - self.mutation_multiply_border, 1 + self.mutation_multiply_border)
else:
row[i] += random.uniform(-self.mutation_addding_border, self.mutation_addding_border)

for _ in range(int(len(vector) / (1 / self.chance_for_mutation))):
item = random.choice(vector.keys())
if random.random() < 0.2:
vector[item] *= random.uniform(1 - self.mutation_multiply_border, 1 + self.mutation_multiply_border)
else:
vector[item] += random.uniform(-self.mutation_addding_border, self.mutation_addding_border)

#Normalisation of vector

normalisation = 100 / sum(vector.values())
for key,value in vector.items():
vector[key] = value * normalisation


return matrix, vector

def create_new_decision_matrix(self, second : Agent):
output = []
for i in range(len(self.weights)):
number = random.randint(0, len(self.weights[0]))
output.append(self.weights[i][:number] + self.weights[i][number:])
return output

def create_new_genome_vector(self, second : Agent):
genome = self.body_points.copy()
for item in genome.keys():
genome[item] = self.body_points[item] if random.randint(0,1) == 1 else second.body_points[item]
return genome

def render(self, window: pygame.window, cell_size: int, offset: tuple):
offset_x, offset_y = offset

Expand Down
7 changes: 7 additions & 0 deletions src/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,3 +16,10 @@
CELL_SIZE = cfg["grid"]["cell_size"]
GRID_WIDTH = PANEL_WIDTH // CELL_SIZE
GRID_HEIGHT = PANEL_HEIGHT // CELL_SIZE

MIN_AGE_PERCENT = cfg["mating"]["min_age_percent"]
MIN_ENERGY_LEVEL = cfg["mating"]["min_energy_level"]
MAX_RANGE = cfg["mating"]["max_range"]
MUTATION_CHANCE = cfg["mating"]["mutation_chance"]
MUTATION_MULTIPLY_BORDER = cfg["mating"]["mutation_multiply_border"]
MUTATION_ADDING_BORDER = cfg["mating"]["mutation_addding_border"]
12 changes: 7 additions & 5 deletions src/environment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,7 +118,7 @@ def _spawn_initial_agents(self):
Agent.bound_y = self.pixel_height
Agent.cell_size = self.cell_size

for _ in range(50):
for _ in range(500):
pos_x = random.randint(0, Agent.bound_x)
pos_y = random.randint(0, Agent.bound_y)
agent = Agent((pos_x, pos_y), self)
Expand Down Expand Up @@ -212,13 +212,15 @@ def render(self, window: pygame.window, panel_x: int, panel_y: int, cell_size: i
agent.render(window, cell_size, (panel_x, panel_y))

font = pygame.font.Font(None, 32)
tick_text = font.render(f"Ticks: {self.tick_counter}", True, (255, 255, 255))
tick_text = font.render(f"Ticks: {self.tick_counter}", True, (255, 0, 0))
food_count_text = font.render(
f"Food items: {len(self.food_items)}", True, (255, 255, 255)
f"Food items: {len(self.food_items)}", True, (255, 0, 0)
)
agent_text = font.render(f"Agents: {len(self.agents)}", True, (255, 0, 0))

window.blit(tick_text, (panel_x + 10, panel_y + 10))
window.blit(food_count_text, (panel_x + 10, panel_y + 90))
window.blit(agent_text, (panel_x + 10, panel_y + 170))

def shutdown(self):
self.running = False
Expand All @@ -230,8 +232,8 @@ def get_agents(self):

#This function should deal with creating new agents
def create_agent(self, agent : Agent):
pass
# self.agents.append(agent)
self.agents.append(agent)

def _get_agent_area(self, agent: Agent) -> Area:
"""Maps agent pixel position to the underlying terrain cell."""
grid_x = min(self.grid_width - 1, max(0, int(agent.x // self.cell_size)))
Expand Down
76 changes: 74 additions & 2 deletions src/mating.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,76 @@

from __future__ import annotations
from agent import Agent, dist2
import typing
import config
from random import choice, randint, random, uniform

class Mating:
pass
def __init__(self, parent: Agent):
self.parent : Agent = parent

def mate(self):
if not self._checking_constrains():
return
close_agents = self._get_nearby_agents()
matrix, vector = self.get_new_genome(choice(close_agents))
new_agent = Agent((self.parent.x, self.parent.y), self.parent.environment, decision_matrix= matrix, genome= vector)
energy_level = self.parent.energy / self.parent.max_energy
new_agent.energy = new_agent.max_energy * energy_level / 2
self.parent.energy /= 2
self.parent.environment.create_agent(new_agent)


def _get_nearby_agents(self):
close_agents: typing.List[Agent] = []
for agent in self.parent.environment.get_agents():
if dist2(self.parent.x, self.parent.y, agent.x, agent.y) < config.MAX_RANGE:
close_agents.append(agent)
return close_agents

def _checking_constrains(self):
return not( self.parent.age < config.MIN_AGE_PERCENT * self.parent.max_age or self.parent.energy < config.MIN_ENERGY_LEVEL * self.parent.max_energy)

def get_new_genome(self, second : Agent):
matrix = self.create_new_decision_matrix(second)
vector = self.create_new_genome_vector(second)

numbers_to_change_in_matrix = int(len(matrix) * len(matrix[0]) / (1 / config.MUTATION_CHANCE))

#Applying mutation - there could be multiply of value or adding

for _ in range(numbers_to_change_in_matrix):
row =choice(matrix)
i = randint(0, len(row)-1)
if random() < 0.2:
row[i] *= uniform(1 - config.MUTATION_ADDING_BORDER, 1 + config.MUTATION_ADDING_BORDER)
else:
row[i] += uniform(-config.MUTATION_MULTIPLY_BORDER, config.MUTATION_MULTIPLY_BORDER)

for _ in range(int(len(vector) / (1 / config.MUTATION_CHANCE))):
item = choice(vector.keys())
if random() < 0.2:
vector[item] *= uniform(1 - config.MUTATION_ADDING_BORDER, 1 + config.MUTATION_ADDING_BORDER)
else:
vector[item] += uniform(-config.MUTATION_MULTIPLY_BORDER, config.MUTATION_MULTIPLY_BORDER)

#Normalisation of vector

normalisation = 100 / sum(vector.values())
for key,value in vector.items():
vector[key] = value * normalisation


return matrix, vector

def create_new_decision_matrix(self, second : Agent):
output = []
for i in range(len(self.parent.weights)):
number = randint(0, len(self.parent.weights[0]))
output.append(self.parent.weights[i][:number] + second.weights[i][number:])
return output

def create_new_genome_vector(self, second : Agent):
genome = self.parent.body_points.copy()
for item in genome.keys():
genome[item] = self.parent.body_points[item] if randint(0,1) == 1 else second.body_points[item]
return genome
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