ants/agent.py

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"""
agent.py - Part of ants project
This model implements the actual agents on the grid (a.k.a. the ants)
License: AGPL 3 (see end of file)
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(C) Alexander Bocken, Viviane Fahrni, Grace Kagho
"""
"""
TO DISCUSS:
Is the separation of energy and sensitivity useful?
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"""
import numpy as np
import numpy.typing as npt
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from mesa.agent import Agent
from mesa.space import Coordinate
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class RandomWalkerAnt(Agent):
def __init__(self, unique_id, model,
look_for_pheromone=None,
drop_pheromone=None,
sensitivity_max = 30000,
) -> None:
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super().__init__(unique_id=unique_id, model=model)
self._next_pos : None | Coordinate = None
self._prev_pos : None | Coordinate = None
self.look_for_pheromone : str|None = look_for_pheromone
self.drop_pheromone : str|None = drop_pheromone
self.energy : float = self.model.e_0
self.sensitivity : float = self.model.s_0
self.pheromone_drop_rate : float = self.model.q_0
self.sensitivity_max = sensitivity_max
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def sens_adj(self, props, key) -> npt.NDArray[np.float_] | float:
"""
returns the adjusted value of any property dependent on the current
sensitivity.
The idea is to have a nonlinear response, where any opinion below a
threshold (here: self.threshold[key]) is ignored, otherwise it returns
the property
Long-term this function should be adjusted to return the property up
to a upper threshold as well.
returns ^
|
sens_max| __________
| /
| /
q^tr| /
|
0|________
-----------------------> prop
"""
# if props iterable create array, otherwise return single value
try:
iter(props)
except TypeError:
# TODO: proper nonlinear response, not just clamping
if props > self.sensitivity_max:
return self.sensitivity_max
if props > self.model.q_tr:
return props
else:
return 0
arr : list[float] = []
for prop in props:
arr.append(self.sens_adj(prop, key))
return np.array(arr)
def _get_resistance_weights(self, positions=None):
if positions is None:
positions = self.neighbors()
# bit round-about but self.model.grid.fields['res'][positions]
# gets interpreted as slices, not multiple singular positions
resistance = np.array([ self.model.grid.fields['res'][x,y] for x,y in positions ])
easiness = np.max(self.model.grid.fields['res']) - resistance + 1e-15 # + epsilon to not divide by zero
weights = easiness/ np.sum(easiness)
return weights
def _choose_next_pos(self):
def _pick_from_remaining_five(remaining_five):
"""
"""
weights = self._get_resistance_weights(remaining_five)
random_index = np.random.choice(range(len(remaining_five)), p=weights)
self._next_pos = remaining_five[random_index]
self._prev_pos = self.pos
if self._prev_pos is None:
weights = self._get_resistance_weights()
i = np.random.choice(range(6),p=weights)
assert(self.pos is not self.neighbors()[i])
self._next_pos = self.neighbors()[i]
self._prev_pos = self.pos
return
if self.searching_food:
for neighbor in self.front_neighbors:
if self.model.grid.is_food(neighbor):
self.model.grid.fields['food'][neighbor] -= 1 # eat
#resets
self.pheromone_drop_rate = self.model.q_0
self.sensitivity = self.model.s_0
self.energy = self.model.e_0
#now look for other pheromone
self.drop_pheromone = "B"
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self.look_for_pheromone = "A"
self._prev_pos = neighbor
self._next_pos = self.pos
return
elif self.searching_nest:
for neighbor in self.front_neighbors:
if self.model.grid.is_nest(neighbor):
#resets
self.pheromone_drop_rate = self.model.q_0
self.sensitivity = self.model.s_0
self.energy = self.model.e_0
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self.look_for_pheromone = "B"
self.drop_pheromone = "A"
self._prev_pos = neighbor
self._next_pos = self.pos
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# recruit new ants
for agent_id in self.model.get_unique_ids(self.model.N_r):
if self.model.schedule.get_agent_count() < self.model.N_m:
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agent = RandomWalkerAnt(unique_id=agent_id, model=self.model, look_for_pheromone="B", drop_pheromone="A")
agent._next_pos = self.pos
self.model.schedule.add(agent)
self.model.grid.place_agent(agent, pos=neighbor)
return
# follow positive gradient with likelihood self.sensitivity
if self.look_for_pheromone is not None:
# Calculate gradient
front_concentration = [self.model.grid.fields[self.look_for_pheromone][cell] for cell in self.front_neighbors ]
front_concentration = self.sens_adj(front_concentration, self.look_for_pheromone)
current_pos_concentration = self.sens_adj(self.model.grid.fields[self.look_for_pheromone][self.pos], self.look_for_pheromone)
gradient = front_concentration - np.repeat(current_pos_concentration, 3).astype(np.float_)
index = np.argmax(gradient)
if gradient[index] > 0:
# follow positive gradient with likelihood self.sensitivity
p = np.random.uniform()
if p < self.sensitivity:
self._next_pos = self.front_neighbors[index]
self._prev_pos = self.pos
else:
other_neighbors = self.neighbors().copy()
other_neighbors.remove(self.front_neighbors[index])
_pick_from_remaining_five(other_neighbors)
return
# do biased random walk
p = np.random.uniform()
# TODO: This completely neglects resistance, relevant?
if p < self.model.alpha:
self._next_pos = self.front_neighbor
self._prev_pos = self.pos
else:
other_neighbors = self.neighbors().copy()
other_neighbors.remove(self.front_neighbor)
_pick_from_remaining_five(other_neighbors)
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def step(self):
self.sensitivity -= self.model.d_s
self.energy -= self.model.grid.fields['res'][self.pos] * self.model.d_e
# Die and get removed if no energy
if self.energy < self.model.e_min:
self.model.schedule.remove(self)
else:
self._choose_next_pos()
self._adjust_pheromone_drop_rate()
def _adjust_pheromone_drop_rate(self):
if(self.drop_pheromone is not None):
self.pheromone_drop_rate -= self.pheromone_drop_rate * self.model.beta
def drop_pheromones(self) -> None:
# should only be called in advance() as we do not use hidden fields
if self.drop_pheromone is not None:
self.model.grid.fields[self.drop_pheromone][self.pos] += self.pheromone_drop_rate
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def advance(self) -> None:
self.drop_pheromones()
self.model.grid.move_agent(self, self._next_pos)
self._next_pos = None # so that we rather crash than use wrong data
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# TODO: find out how to decorate with property properly
def neighbors(self, pos=None, include_center=False):
if pos is None:
pos = self.pos
return self.model.grid.get_neighborhood(pos, include_center=include_center)
@property
def searching_nest(self) -> bool:
return self.drop_pheromone == "B"
@property
def searching_food(self) -> bool:
return self.drop_pheromone == "A"
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@property
def front_neighbors(self):
"""
returns all three neighbors which the ant can see
"""
all_neighbors = self.neighbors()
neighbors_at_the_back = self.neighbors(pos=self._prev_pos, include_center=True)
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front_neighbors = list(filter(lambda i: i not in neighbors_at_the_back, all_neighbors))
########## DEBUG
try:
assert(self._prev_pos is not None)
assert(self._prev_pos is not self.pos)
assert(self._prev_pos in all_neighbors)
assert(len(front_neighbors) == 3)
except AssertionError:
print(f"{self._prev_pos=}")
print(f"{self.pos=}")
print(f"{all_neighbors=}")
print(f"{neighbors_at_the_back=}")
print(f"{front_neighbors=}")
raise AssertionError
else:
return front_neighbors
@property
def front_neighbor(self):
"""
returns neighbor of current pos
which is towards the front of the ant
"""
neighbors__prev_pos = self.neighbors(self._prev_pos)
for candidate in self.front_neighbors:
# neighbor in front direction only shares current pos as neighborhood with _prev_pos
candidate_neighbors = self.model.grid.get_neighborhood(candidate)
overlap = [x for x in candidate_neighbors if x in neighbors__prev_pos]
if len(overlap) == 1:
return candidate
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"""
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, version 3.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see <https://www.gnu.org/licenses/>
"""