Merge branch 'implement_agent_step'
Initial implementation of step() accoriding to the rules of Schweitzer et alii 1996. A few neighbor fetching functions are now implemented in agent.py. The MultiHexScalarFields implementation could be simplified as the ants only drop their pheromones in advance() and not step(), thus they do not interfere with each other. We might need to look at the concentration decay for the scalar fields again to correctly implement it. To me it is not clear whether we should decrease the pheromone levels before the ants step() or after the ants step() and before their advance()
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81
agent.py
81
agent.py
@ -9,17 +9,19 @@ License: AGPL 3 (see end of file)
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import numpy as np
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from mesa.agent import Agent
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from mesa.space import Coordinate
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from typing import overload
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class RandomWalkerAnt(Agent):
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def __init__(self, unique_id, model, do_follow_chemical_A=True,
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def __init__(self, unique_id, model, look_for_chemical=None,
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energy_0=1, chemical_drop_rate_0=1, sensitvity_0=1, alpha=0.5) -> None:
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super().__init__(unique_id=unique_id, model=model)
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self._next_pos : None | Coordinate = None
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self.prev_pos : None | Coordinate = None
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self.do_follow_chemical_A : bool = True # False -> follow_chemical_B = True
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self.look_for_chemical = look_for_chemical
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self.drop_chemical = None
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self.energy : float = energy_0
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self.sensitvity : float = sensitvity_0
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self.chemical_drop_rate : float = chemical_drop_rate_0 #TODO: check whether needs to be separated into A and B
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@ -30,39 +32,68 @@ class RandomWalkerAnt(Agent):
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# TODO
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return prop
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def step(self):
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# Calculate where next ant location should be and store in _next_pos
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# TODO
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pass
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# follow positive gradient
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if self.look_for_chemical is not None:
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front_concentration = [self.model.grid.fields[self.look_for_chemical][cell] for cell in self.front_neighbors ]
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gradient = front_concentration - np.repeat(self.model.grid.fields[self.look_for_chemical][self.pos], 3)
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index = np.argmax(gradient)
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if gradient[index] > 0:
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self._next_pos = self.front_neighbors[index]
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return
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def drop_chemicals(self):
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# drop chemicals (depending on current state) on concentration field
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# TODO
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# use self.model.grid.add_to_field(key, value, pos) to not interfere with other ants
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pass
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# do biased random walk
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p = np.random.uniform()
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if p < self.alpha:
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self._next_pos = self.front_neighbor
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else:
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# need copy() as we would otherwise remove the tuple from all possible lists (aka python "magic")
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other_neighbors = self.neighbors().copy()
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other_neighbors.remove(self.front_neighbor)
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random_index = np.random.choice(range(len(other_neighbors)))
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self._next_pos = other_neighbors[random_index]
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def drop_chemicals(self) -> None:
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# should only be called in advance() as we do not use hidden fields
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if self.drop_chemical is not None:
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self.model.grid.fields[self.drop_chemical][self.pos] += self.chemical_drop_rate
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def advance(self) -> None:
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self.drop_chemicals()
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self.prev_pos = self.pos
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self.pos = self._next_pos
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# TODO: find out how to decorate with property properly
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def neighbors(self, pos=None, include_center=False):
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if pos is None:
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pos = self.pos
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return self.model.grid.get_neighborhood(pos, include_center=include_center)
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@property
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def front_neighbors(self):
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if self.prev_pos is not None:
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assert(self.pos is not None)
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x, y = self.pos
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x_prev, y_prev = self.prev_pos
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dx, dy = x - x_prev, y - y_prev
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front = np.array([
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(x, y + dy),
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(x + dx, y + dy),
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(x + dx, y),
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])
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return front #TODO: verify (do we need to sperate into even/odd?)
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else:
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# TODO: return all neighbors or raise Exception?
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pass
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"""
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returns all three neighbors which the ant can see
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"""
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assert(self.prev_pos is not None)
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all_neighbors = self.neighbors()
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neighbors_at_the_back = self.neighbors(pos=self.prev_pos, include_center=True)
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return list(filter(lambda i: i not in neighbors_at_the_back, all_neighbors))
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@property
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def front_neighbor(self):
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"""
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returns neighbor of current pos
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which is towards the front of the ant
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"""
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neighbors_prev_pos = self.neighbors(self.prev_pos)
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for candidate in self.front_neighbors:
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# neighbor in front direction only shares current pos as neighborhood with prev_pos
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candidate_neighbors = self.model.grid.get_neighborhood(candidate)
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overlap = [x for x in candidate_neighbors if x in neighbors_prev_pos]
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if len(overlap) == 1:
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return candidate
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"""
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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.
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20
main.py
20
main.py
@ -27,13 +27,27 @@ def main():
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max_steps=max_steps)
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# just initial testing of MultiHexGrid
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a = model.agent_density()
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for loc in model.grid.iter_neighborhood(nest_position):
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a[loc] = 3
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for agent in model.grid.get_neighbors(pos=nest_position, include_center=True):
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if agent.unique_id == 2:
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agent.do_follow_chemical_A = False
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agent.look_for_chemical = "A"
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agent.prev_pos = (9,10)
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print(agent.front_neighbors)
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a[agent.prev_pos] = 1
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for pos in agent.front_neighbors:
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a[pos] = 6
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agent.step()
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print(f"{agent._next_pos=}")
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agent.advance()
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print(agent.front_neighbor)
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a[agent.front_neighbor] = 5
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print(agent.pos, agent.unique_id, agent.do_follow_chemical_A)
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print(agent.pos, agent.unique_id, agent.look_for_chemical)
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neighbors = model.grid.get_neighborhood(nest_position)
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print(neighbors)
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print(a)
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if __name__ == "__main__":
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17
model.py
17
model.py
@ -11,11 +11,10 @@ License: AGPL 3 (see end of file)
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import numpy as np
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from mesa.model import Model
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from mesa.space import Coordinate, HexGrid, Iterable
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from multihex import MultiHexGrid, MultiHexGridScalarFields
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from multihex import MultiHexGridScalarFields
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from mesa.time import SimultaneousActivation
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from mesa.datacollection import DataCollector
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from agent import RandomWalkerAnt
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from agent import Pheromone
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class ActiveWalkerModel(Model):
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# TODO: separate food and source into new agents?
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@ -25,11 +24,7 @@ class ActiveWalkerModel(Model):
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nest_position : Coordinate,
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max_steps:int=1000) -> None:
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super().__init__()
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fields={"A" : True, # key : also have _next prop (for no interference in step)
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"B": True,
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"nests": False,
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"food" : False,
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}
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fields=["A", "B", "nests", "food"]
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self.schedule = SimultaneousActivation(self)
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self.grid = MultiHexGridScalarFields(width=width, height=height, torus=True, fields=fields)
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self._unique_id_counter = -1
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@ -43,7 +38,7 @@ class ActiveWalkerModel(Model):
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}
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for agent_id in self.get_unique_ids(num_initial_roamers):
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agent = RandomWalkerAnt(unique_id=agent_id, model=self, do_follow_chemical_A=True)
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agent = RandomWalkerAnt(unique_id=agent_id, model=self, look_for_chemical="A")
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self.schedule.add(agent)
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self.grid.place_agent(agent, pos=nest_position)
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@ -53,6 +48,12 @@ class ActiveWalkerModel(Model):
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)
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self.datacollector.collect(self) # keep at end of __init___
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def agent_density(self):
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a = np.zeros((self.grid.width, self.grid.height))
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for i in range(self.grid.width):
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for j in range(self.grid.height):
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a[i,j] = len(self.grid[(i,j)])
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return a
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def step(self):
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19
multihex.py
19
multihex.py
@ -93,32 +93,17 @@ class MultiHexGrid(HexGrid):
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class MultiHexGridScalarFields(MultiHexGrid):
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def __init__(self, fields: dict[str, bool], width : int, height : int, torus : bool, scalar_initial_value : float=0) -> None:
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def __init__(self, fields: list[str], width : int, height : int, torus : bool, scalar_initial_value : float=0) -> None:
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super().__init__(width=width, height=height, torus=torus)
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self._field_props = fields
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self.fields : dict[str, npt.NDArray[np.float_]] = {}
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for key, is_step_field in fields.items():
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for key in fields:
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self.fields[key] = np.ones((width, height)).astype(float) * scalar_initial_value
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if is_step_field:
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self.fields[f"_next_{key}"] = np.zeros((width, height)).astype(float)
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def reset_field(self, key : str) -> None:
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self.fields[key] = np.zeros((self.width, self.height))
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def add_to_field(self, field_key : str, value : float, pos : Coordinate) -> None:
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if self._field_props[field_key]:
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self.fields[f"_next_{field_key}"][pos] += value
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else:
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self.fields[field_key][pos] += value
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def step(self) -> None:
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for key, is_step_field in self._field_props.items():
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if is_step_field:
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self.fields[key] += self.fields[f"_next_{key}"]
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self.reset_field(f"_next_{key}")
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"""
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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.
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