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Merged
SzeMrok merged 1 commit into from
Jan 24, 2026
Merged

Changing algorithm of agents movements and sense #33

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6 changes: 3 additions & 3 deletions config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,6 @@ mating:
min_age_percent: 0.10
min_energy_level: 0.50
max_range: 10
mutation_chance: 0.08
mutation_multiply_border: 0.25
mutation_addding_border: 0.05
mutation_chance: 0.05
mutation_multiply_border: 0.1
mutation_addding_border: 0.1
216 changes: 46 additions & 170 deletions src/agent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,10 +77,8 @@ class Agent:


actions = {"ACTION_MOVE" : 0,
"ACTION_IDLE" : 1,
"ACTION_FLEE" : 2,
"ACTION_MATE" : 3,
"ACTION_ATTACK" : 4}
"ACTION_MATE" : 1,
"ACTION_ATTACK" : 2}

body_points_total = 100

Expand All @@ -89,9 +87,11 @@ class Agent:

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

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

Expand All @@ -106,7 +106,7 @@ def __init__(self, position: tuple, environment : Environment,/, decision_matrix

self.max_hp = 10.0 + _sqrt_scale(self.body_points["hp"], 2.0)
self.max_energy = (10.0 + _sqrt_scale(self.body_points["energy"], 2.0)) * 2
self.base_speed = 0.5 + _sqrt_scale(self.body_points["speed"], 0.2)
self.base_speed = 0.70+ _sqrt_scale(self.body_points["speed"], 0.28)
self.attack_power = 0.5 + _sqrt_scale(self.body_points["attack"], 0.06)
self.max_age = int((300 + _sqrt_scale(self.body_points["lifespan"], 18.0)) * 2.5)
self.sight = 70.0 + _sqrt_scale(self.body_points["sight"], 6.0)
Expand All @@ -116,19 +116,20 @@ def __init__(self, position: tuple, environment : Environment,/, decision_matrix
self.energy = self.max_energy
self.age = 0

self.angle = random.random() * 360.0
self.last_action = self.actions["ACTION_MOVE"]

self.input_size = len(self.sense())
self.action_count = len(self.actions)
self.output_size = self.action_count + 2
self.angle = random.random() * 360

if decision_matrix:
self.weights = decision_matrix
else:
self.weights = self._random_matrix(self.input_size, self.output_size, scale=0.6)



self._inputs_override = None
self._last_enemy = None
self._last_food = None
Expand All @@ -155,134 +156,13 @@ def set_inputs(self, inputs):
self._inputs_override = [float(v) for v in inputs]

def sense(self, foods=None, agents=None):
"""Generate normalized sensory input vector."""
bx = float(self.bound_x) if self.bound_x > 0 else 1.0
by = float(self.bound_y) if self.bound_y > 0 else 1.0

hp_n = _clamp(self.hp / max(1e-9, self.max_hp), 0.0, 1.0)
en_n = _clamp(self.energy / max(1e-9, self.max_energy), 0.0, 1.0)
age_n = _clamp(self.age / max(1, self.max_age), 0.0, 1.0)

x_n = _clamp(self.x / max(1e-9, bx), 0.0, 1.0) # normalized position
y_n = _clamp(self.y / max(1e-9, by), 0.0, 1.0)

ang = math.radians(self.angle)
head_x = math.cos(ang) # facing direction (unit vector)
head_y = -math.sin(ang)

food_d_n, food_dx_n, food_dy_n, food_in_sight = 1.0, 0.0, 0.0, 0
if foods:
best_d2 = 1e18
best = 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)
if d2 < best_d2:
best_d2 = d2
best = (fx, fy)
if best is not None:
dx, dy = torus_diff(self.x, self.y, best[0], best[1])
d = math.sqrt(best_d2)
food_d_n = _clamp(d / max(1e-9, self.sight), 0.0, 1.0)
food_dx_n = _clamp(dx / max(1e-9, self.sight), -1.0, 1.0)
food_dy_n = _clamp(dy / max(1e-9, self.sight), -1.0, 1.0)
self._last_food = best
if d <= self.sight: # Adding boolean to deal with semantic discontinuity ( 1 could mean food is far away and 0,95 mean is really close)
food_in_sight = 1

friend_count_n = 0.0
friend_d_n, friend_dx_n, friend_dy_n = 1.0, 0.0, 0.0
enemy_count_n = 0.0
enemy_d_n, enemy_dx_n, enemy_dy_n = 1.0, 0.0, 0.0

if agents:
r2 = self.sight * self.sight
friends = 0
enemies = 0
best_friend_d2 = 1e18
best_friend = None
best_enemy_d2 = 1e18
best_enemy = None

for a in agents:
if a is self:
continue
ax = float(getattr(a, "x", 0.0))
ay = float(getattr(a, "y", 0.0))
d2 = dist2(self.x, self.y, ax, ay)
if d2 > r2:
continue
return self.sense_module.sense(foods, agents)

same_group = getattr(a, "group_id", None) == self.group_id
if same_group:
friends += 1
if d2 < best_friend_d2:
best_friend_d2 = d2
best_friend = (ax, ay)
else:
enemies += 1
if d2 < best_enemy_d2:
best_enemy_d2 = d2
best_enemy = (ax, ay)

friend_count_n = _clamp(friends / 10.0, 0.0, 1.0)
enemy_count_n = _clamp(enemies / 10.0, 0.0, 1.0)
def think(self, inputs):
"""Forward pass through neural network."""

if best_enemy is not None:
dx, dy = torus_diff(self.x, self.y, best_enemy[0], best_enemy[1])
d = math.sqrt(best_enemy_d2)
enemy_d_n = _clamp(d / max(1e-9, self.sight), 0.0, 1.0)
enemy_dx_n = _clamp(dx / max(1e-9, self.sight), -1.0, 1.0)
enemy_dy_n = _clamp(dy / max(1e-9, self.sight), -1.0, 1.0)
self._last_enemy = best_enemy
else:
self._last_enemy = None


if best_friend is not None: # Adding this part to enable to agent getting information where are friends
dx, dy = torus_diff(self.x, self.y, best_friend[0], best_friend[1])
d = math.sqrt(best_friend_d2)
friend_d_n = _clamp(d / max(1e-9, self.sight), 0.0, 1.0)
friend_dx_n = _clamp(dx / max(1e-9, self.sight), -1.0, 1.0)
friend_dy_n = _clamp(dy / max(1e-9, self.sight), -1.0, 1.0)
self._last_friend = best_friend
else:
self._last_friend = None

cell = self.environment._get_agent_area(self)
if cell == Area.PLAINS: ground = 0
elif cell == Area.FERTILE_VALLEY: ground = 0.9
elif cell == Area.DESERT: ground = 0.1
elif cell == Area.BERRY_CORNER: ground= 1
else: raise ValueError(f"Wrong place! {cell}, {type(cell)}")

return [
hp_n,
en_n,
age_n,
x_n,
y_n,
head_x,
head_y,
food_d_n,
food_dx_n,
food_dy_n,
food_in_sight,
friend_count_n,
friend_d_n,
friend_dx_n,
friend_dy_n,
enemy_count_n,
enemy_d_n,
enemy_dx_n,
enemy_dy_n,
ground,
1.0
]

def think(self, inputs):
"""Forward pass through neural network."""
out = [0.0 for _ in range(self.output_size)]
for i in range(self.input_size):
xi = inputs[i]
Expand All @@ -305,51 +185,51 @@ def decide(self, outputs):
intensity = outputs[self.action_count + 1]
return best_i, turn, intensity

def _apply_bounds(self, new_x: float, new_y: float, new_angle: float):
"""Apply torus effect (link right to left and top to bottom)."""
bx = float(self.bound_x)
by = float(self.bound_y)
if bx <= 0 or by <= 0:
self.x, self.y, self.angle = new_x, new_y, new_angle % 360.0
return

max_x = max(0.0, bx - 1e-6)
max_y = max(0.0, by - 1e-6)

self.x = (new_x + bx) % max_x
self.y = (new_y + by) % max_y
self.angle = new_angle % 360.0

def _move(self, turn: float, intensity: float, speed_modifier: float = 1.0):
turn_delta = turn * (self.agility * 0.5)
new_angle = self.angle + turn_delta

turn = (turn + 1) * math.pi

inten = _clamp(0.5 + 0.5 * intensity, 0.0, 1.0)
sp = self.base_speed * (0.20 + 1.30 * inten) * speed_modifier

rad = math.radians(new_angle)
new_x = self.x + math.cos(rad) * sp
new_y = self.y - math.sin(rad) * sp
self._apply_bounds(new_x, new_y, new_angle)
self.x = (self.x + math.cos(turn) * sp) % (self.environment.grid_width * self.environment.cell_size)
self.y = (self.y - math.sin(turn) * sp) % (self.environment.grid_height * self.environment.cell_size)

cost = 0.01 + 0.05 * inten
cost = 0.0075 + 0.035 * inten
self.energy = max(0.0, self.energy - cost)

def _idle(self):
self.energy = min(self.max_energy, self.energy - 0.01)
self.angle = math.degrees(turn)


def _flee(self, turn: float, intensity: float):
if self._last_enemy is None:
self._move(turn, intensity)
return
ex, ey = self._last_enemy
dx, dy = torus_diff(ex, ey, self.x, self.y)
ang = math.degrees(math.atan2(-dy, dx))
self.angle = ang % 360.0
self._move(0.0, max(0.2, intensity))

def _attack(self):
self.energy = max(0.0, self.energy - 0.08)
self.energy = max(0.0, self.energy - 0.16)
agents = self.environment.agents
if agents:
r2 = self.sight * self.sight
best_enemy_d2 = 1e18
best_enemy = None

for a in agents:
if a is self:
continue
ax = float(getattr(a, "x", 0.0))
ay = float(getattr(a, "y", 0.0))
d2 = dist2(self.x, self.y, ax, ay)
if d2 > r2:
continue
same_group = getattr(a, "group_id", None) == self.group_id
if same_group:
continue
else:
if d2 < best_enemy_d2:
best_enemy_d2 = d2
best_enemy : Agent = a
if best_enemy:
best_enemy.hp -= self.attack_power



def _mate(self):
self.mate_module.mate()
Expand Down Expand Up @@ -378,24 +258,20 @@ def update(self, speed_modifier):

outputs = self.think(inputs)
action, turn, intensity = self.decide(outputs)
self.last_action = action

if action == self.actions["ACTION_MOVE"]:
self.logger.debug(f"Agent - {self.uuid}; action - move")
self._move(turn, intensity)
elif action == self.actions["ACTION_IDLE"]:
self.logger.debug(f"Agent - {self.uuid}; action - idle")
self._idle()
elif action == self.actions["ACTION_FLEE"]:
self.logger.debug(f"Agent - {self.uuid}; action - free")
self._flee(turn, intensity)
elif action == self.actions["ACTION_MATE"]:
self.logger.debug(f"Agent - {self.uuid}; action - mate")
self._mate()
else:
self.logger.debug(f"Agent - {self.uuid}; action - attack")
self._attack()

self.last_action = action / 3


self._tick_body()

def render(self, window: pygame.window, cell_size: int, offset: tuple):
Expand Down
4 changes: 2 additions & 2 deletions src/mating.py
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Expand Up @@ -49,9 +49,9 @@ def get_new_genome(self, second : Agent) -> typing.Tuple[typing.List[typing.List
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)
row[i] *= uniform(1 - config.MUTATION_MULTIPLY_BORDER, 1 + config.MUTATION_MULTIPLY_BORDER)
else:
row[i] += uniform(-config.MUTATION_MULTIPLY_BORDER, config.MUTATION_MULTIPLY_BORDER)
row[i] += uniform(-config.MUTATION_ADDING_BORDER, config.MUTATION_ADDING_BORDER)

for _ in range(int(len(vector) / (1 / config.MUTATION_CHANCE))):
item = choice(vector.keys())
Expand Down
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