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develop_gk
Author | SHA1 | Date | |
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61e92ae136 |
60
agent.py
60
agent.py
@ -19,7 +19,9 @@ class RandomWalkerAnt(Agent):
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alpha=0.6, drop_pheromone=None,
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betas : dict[str, float]={"A": 0.0512, "B": 0.0512},
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sensitivity_decay_rate=0.01,
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sensitivity_max = 300
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sensitivity_max = 300,
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sensitivity_min = 0.001,
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sensitivity_steepness = 1
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) -> None:
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super().__init__(unique_id=unique_id, model=model)
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@ -35,9 +37,12 @@ class RandomWalkerAnt(Agent):
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self.pheromone_drop_rate = pheromone_drop_rate_0
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self.alpha = alpha
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self.sensitivity_max = sensitivity_max
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self.sensitivity_min = sensitivity_min
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self.sensitivity_decay_rate = sensitivity_decay_rate
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self.sensitivity_steepness = sensitivity_steepness
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self.betas = betas
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self.threshold : dict[str, float] = {"A": 0, "B": 0}
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def sens_adj(self, props, key) -> npt.NDArray[np.float_] | float:
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@ -60,18 +65,50 @@ class RandomWalkerAnt(Agent):
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0|________
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-----------------------> prop
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For the nonlinear sensitivity, the idea is to use a logistic function that has
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a characteristic sigmoidal shape that starts from a low value, increases rapidly,
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and then gradually approaches a saturation level.
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f(x) = L / (1 + exp(-k*(x - x0)))
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f(x) = return value
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L = sens_max
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k is a parameter that controls the steepness of the curve. We can start with 1
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A higher value of k leads to a steeper curve.
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x0 is the midpoint of the curve, where the sensitivity starts to increase significantly.
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We can make X0 the threshold value
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"""
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# if props iterable create array, otherwise return single value
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try:
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iter(props)
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except TypeError:
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# TODO: proper nonlinear response, not just clamping
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if props > self.sensitivity_max:
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return self.sensitivity_max
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if props > self.threshold[key]:
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return props
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#TODO: proper nonlinear response, not just clamping
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non_linear_sens = True
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if non_linear_sens:
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L = self.sensitivity_max
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k = self.sensitivity_steepness
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mid = self.threshold[key]
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if props > self.sensitivity_max:
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return self.sensitivity_max
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#Should we still keep these conditions?
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# if props > self.threshold[key]:
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# return props
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else:
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adjusted_sensitivity = L / (1 + np.exp(-k * (props - mid)))
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print(f'props: {props}, adjusted_value: {adjusted_sensitivity}')
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return adjusted_sensitivity
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else:
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return 0
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if props > self.sensitivity_max:
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return self.sensitivity_max #Should we still keep these conditions
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if props > self.threshold[key]:
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return props
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else:
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return 0
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arr : list[float] = []
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for prop in props:
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@ -146,6 +183,10 @@ class RandomWalkerAnt(Agent):
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self._choose_next_pos()
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self._adjust_pheromone_drop_rate()
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#kill agent if sensitivity is low
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if self.sensitivity < self.sensitivity_min:
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self._kill_agent()
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def _adjust_pheromone_drop_rate(self):
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if(self.drop_pheromone is not None):
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self.pheromone_drop_rate[self.drop_pheromone] -= self.pheromone_drop_rate[self.drop_pheromone] * self.betas[self.drop_pheromone]
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@ -155,6 +196,11 @@ class RandomWalkerAnt(Agent):
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if self.drop_pheromone is not None:
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self.model.grid.fields[self.drop_pheromone][self.pos] += self.pheromone_drop_rate[self.drop_pheromone]
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def _kill_agent(self):
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#update dead_agent list
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self.model.dead_agents.append(self)
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def advance(self) -> None:
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self.drop_pheromones()
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self.model.grid.move_agent(self, self._next_pos)
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10
model.py
10
model.py
@ -38,6 +38,8 @@ class ActiveWalkerModel(Model):
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self.decay_rates : dict[str, float] = {"A" :0.01,
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"B": 0.01,
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}
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self.dead_agents = []
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for agent_id in self.get_unique_ids(num_initial_roamers):
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if self.schedule.get_agent_count() < self.num_max_agents:
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@ -74,6 +76,14 @@ class ActiveWalkerModel(Model):
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if self.schedule.steps >= self.max_steps:
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self.running = False
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#remove dead agents
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for agent in self.dead_agents:
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self.schedule.remove(agent)
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self.grid.remove_agent(agent)
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self.dead_agents.remove(agent)
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self.dead_agents = []
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#ToDo what happens when all agents die
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def get_unique_id(self) -> int:
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self._unique_id_counter += 1
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